{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 4 PLOTS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "# working directory  <----- change before running the code\n",
    "input_dir = \"~/Dropbox/IT_Revolution/Replication_package/JPE submission/Clean_data\"\n",
    "output_dir = \"~/Dropbox/IT_Revolution/Replication_package/JPE submission/Output\"\n",
    "output_dir = os.path.expanduser(output_dir)\n",
    "\n",
    "# function create scatter plot with confidence interval\n",
    "def plot_confidence_interval_emp(x, coef, top, bottom, color='blue', horizontal_line_width=0.25):   \n",
    "    left = x - horizontal_line_width / 2   #length of ticker mark for CI\n",
    "    right = x + horizontal_line_width / 2\n",
    "    plt.plot([x, x], [top, bottom], color=color)\n",
    "    plt.plot([left, right], [top, top], color=color)\n",
    "    plt.plot([left, right], [bottom, bottom], color=color)\n",
    "    plt.plot(x, coef, 'o', color='blue', ms=8, label=\"Employment\")   # or ks for square marker, ms: marker size\n",
    "    return coef, top, bottom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FIGURE 2 (BAR PLOT)\n",
    "* step 4.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "df_early = pd.read_stata(input_dir + '/exp_dist_plotManuf.dta')   \n",
    "df_late = pd.read_stata(input_dir + '/exp_dist_plotICT.dta')  \n",
    "\n",
    "# get necessary columns only\n",
    "df_early1 = df_early[['high_exp','high_dist','dlemp1940']].copy()\n",
    "df_late1 = df_late[['high_exp','high_dist','dlemp2018']].copy()\n",
    "\n",
    "#Reshape from long to wide \n",
    "df_early1wide = pd.pivot(df_early1, index=['high_exp'], columns = 'high_dist',values = 'dlemp1940') \n",
    "df_late1wide = pd.pivot(df_late1, index=['high_exp'], columns = 'high_dist',values = 'dlemp2018') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d2rVrp1mzZhW57b6ycOFCa/SRdPZUSUlq3bq10+ioqVOnFqnenj17ymazWbc9e/Z4pb2+FBUVpS+++EKRkZGSzo6mHjp0aIF9muull17S4sWLreXY2FhddtllbuPl5OTo5ptv1oMPPujUB44OHjyoe+65R7fccot1iqw777//vtq3b6+5c+c6nYruKCsrSz/++KM6derk0UVsFi1apHbt2umLL75Qenp6oWXT09P1008/6aeffvKovb6WlJSk7t2765lnntGhQ4c8WmfevHlq1qyZ3n777UIvGLV+/Xr16dPHZ6MiSyIuLk7t2rXTuHHjCt1e7tq1S//617/Uu3dvt9tgX5o0aZLT8lNPPaWoqKgAtSb0xMfHOy27OgXflbzlvvvuuwLLOl7wzdOLYuXdV9esWdNlufj4eG3bts1a7t27t2w2m9v6844ynj9/vkft8qW8F9gs7H/lOPo2MjJS3bp1c1t/s2bNnM4eCNRr9sd7DgDgX5yvCQDnuKysLKflsLAwl+WqVq2qrl27qmPHjqpZs6aioqJ0/PhxrVmzRt99951SU1MlSStWrNDNN9+sX3/91ePTy8+cOaNrr71W27ZtU9myZTVgwABddtllqlq1qg4fPqw5c+Zo9erVkqTMzEzdeeed2rZtm+rUqVNovSNHjtS7775rLdvtdl111VXq3r27atWqpdOnT2vFihX69ttvlZGRoYyMDN1yyy2KiorSNddc41HbfckxMduxY0enKSSGDRtmnfL6/fff6/Dhw6pdu7bf2+hP7dq106RJk3T//fdLOnv17aeffjpfcinXH3/8oXHjxlnL3bt317///W+PYj333HP66quvJEktWrTQTTfdpMaNG+v06dNatmyZ5s+fb312Pv/8c1WqVEnvvPNOoXV+8MEHuvfee50e69Kli66++mrVqlVLR48e1cKFC605j7Ozs/Xwww/LGKOHH37YZZ0JCQm64YYbrKuXS9Jll12myy+/XA0bNlTZsmV1+vRp7d27V+vXr9dvv/3mNuntT3feeaf++OMPSdLFF1+sfv36qV69ekpJSdGGDRtUrlw5p/KfffaZ7rjjDmvaF0nq1KmT+vbtq5iYGGVnZ+uvv/7SrFmzdOzYMUnS66+/rvDwcGvakeKKioqyfvD6559/rNPBK1SoUGDiyjEplmv//v3q1q2bDh8+bD1Wt25dDR48WC1btlRGRobWr1+vr7/+WqdPn5Z09nTmK6+8UsuXL/f7FBjGGP3www/WcqVKlTR48GC/tiHUnTx50mnZ0ylW8pbbsGFDgWUvu+wy68fR33//XWlpadYPZgX59ddfnZYLSlBu3LjRafmSSy4ptN5cF110kcLDw61t7aZNmzxaz5fyJjYL2+86trdz586FThnh6LLLLrPi7N27V4mJiX6fN9of7zkAgJ8FeggwAOCs4k6h0LZtW6f19u3b51Rnjx49zPz5801GRkaBdSQkJJhrrrnGqZ5p06YVGtdxCoXcKzR36NDB7Ny502V5x1OvJZnHH3+80Po/+ugjp/Lnn3++2bp1q8uy27dvN82bN7fK1qxZ0xw9erTAuv0xhUJCQoLTKfZ5r9h+7NgxpykhJk6c6HHdjv97SSY+Pt6rbXes2xenOl977bVW/TabzSxYsCBfmVOnTplGjRpZ5apWrWr2799fYJ15Pz+578mnn37a5ZW016xZY+rXr+/xKaZ///23iYqKsspGR0eb2bNnuyw7f/58p2lEypYtW+B0Co6fi8jISPPjjz8W2AZjjElMTDTvvfeemTp1qkf/B08VZwqF3Fv58uXN3Llz3cbYunWriY6OdurTgk4JP3nyZL73yZIlSzx+Pe64mgLGEzk5OeaKK65wev133323SUlJyVf2n3/+MV26dHEqO2bMGK+9Bk+nUNi4caNTuX79+nmtDUXh6+2KL5V0CoUHH3ywWKel5+27cuXKFTh1wS+//OJUNnfKnoLs2rXLVKtWzSo/cODAAsu++OKLxT6dv2HDhtZ6LVu29Hg9X0hJSXGaTqpOnToF/j8PHjzo0efLlbFjxzqt+8cffxS5rSWdQmHgwIFO6yclJXm03ty5c53Wa926dZHbDgDwDaZQAIBz2Pr16/XXX39Zy3Xr1lWDBg2s5SFDhmjJkiXq379/oSNHatSooW+++cbp1PQ33njD43bk5OSoQYMGWrRokZo1a+ayzLhx45zq/+yzzwqsLykpSQ899JC13KpVKy1btqzACwK1bNlSv/76qypXrizp7KhGT05d96UZM2ZYp9iHh4frlltucXq+WrVqTlcAD6WrPU+bNk3169eXdHZ04LBhw3TkyBGnMvfee6/T1BAffvihtY4ncnJyNGLECI0fP97lqPTOnTvrxx9/dLog0OOPP15gff/+97+tUeqSNHPmTF1//fUuy/bv319ffvmltZyRkaEnn3zSZdlFixZZ90eMGKG+ffsW/KIkVaxYUcOHD9fdd99daDl/mjVrlgYOHOi23H333WeNNI6OjtaiRYsKHClfuXJlff3117r44oslnX2fPPPMM95rdDH98MMPTqMWr7/+en344YdOV57PVbduXX3//fdq3ry59dirr75a4JQevrJq1Sqn5Ysuusiv8XF2H+so7yjQguzevdtpOSUlJd+2MtcVV1yhBx980FqeNGmSBg8erHXr1lkX0pTOjsx87733dMkll+j48eOSzk7F9N5773ncjoYNG3rU/rxl89bjb++++67TVCaDBg0qcCoIb71mV3X5g7fec3FxcV5rEwCgZEjgAsA56vjx4xo2bJjTYzfeeKPTsrvTJx2VKVNGL7zwgrW8fv36Ag8UXZk4caLbU/SGDx9u3f/nn38KTGR8+OGHSkxMtJanTp1a4FWiczVo0EBPP/20tfzBBx84nabtb44J2b59+7o8Rdux//7++2/9/vvvfmlbUSxdutRpvl1Pbj179iy0zqpVq+rTTz+1pug4cuSIhg0bZiUZpk6d6pQAHTFiRIHJ0sJiTJgwodAybdu2dUp4rF+/XmvXrs1X7uDBg07zNg8cOFDXXXddoXVfffXVuummm6zlBQsWaNeuXfnKOZ6G36JFi0LrDEYDBw5Uv3793JZbv36901zGzzzzjM4///xC1wkPD3f6Iem3335z+sEqEN5++23rflRUlN54441C5wKtVKmSXn/9dWs5KytL77//vk/bmFfe7bjj/JzwjwsvvNBp+ZdffvFoPVflCptLefLkyXrxxRet+Y1nzZqlzp07q0KFCmrUqJFq166tatWqacSIETp69KhsNptuvvlmrVixotCpBPLGdDW1SEEcvxdkZma6nefbV3bu3KmxY8day9HR0YX+aOet1yzJmkrFn7z1nsudngoAEHgkcAHgHJKWlqYdO3bov//9r84//3xt3rzZeq5ixYpOCczi6Natm9OIxNx5a90pX768U7KqIHnnzXO8KIojx9G5F198sUcXrZL+d5Ew6WzSIlDz7f3xxx/aunWrtZw30Z7r6quvdhol4+nFzJYsWSJjjHVr1KhRidobCD169HB6vy5cuFCvvvqq/v77b6fR123atNFrr71W5PoHDx7s0ZyDjj8qSK4v2LJgwQKnuaZHjRrlURvuu+8+677JMw9pLseRmytWrPCo3mBy1113eVRu5syZ1v2yZcs6/W8Kc+GFFzqNvP/555+L1kAvSk9Pd0puXHvttapXr57b9a6++uqAXtQod5RlrtwzFeA/Xbt2dbp41owZM5SQkFDoOocPH9Ynn3yS7/HCkoE2m01PPfWU4uLidMMNN1iPp6SkaO/evTpy5Ij1Q1m1atX00Ucf6csvvyxwDuhceS92VpQfh/NeLK+gi5z6UnJysm688UanC5a9+OKLhZ7Vca6/5quuusrpOgZvvfWW01kkrmzevFk//vhjvscDkYAGAORHAhcAgtRdd92Vb2RjVFSUWrZsqYcffthp9GpkZKTmzJlT4otghYeHq3r16tayp6f6enpxj7zJDldXoE9OTna6aIbjNAPu1KpVSzExMdbyn3/+6fG63uSYiK1SpUqBp4mXKVNGQ4cOtZZnzZoVdAdKkZGRatq0aZFuniS1pPzTajz99NO65pprrIPsyMhIff755/kOhj3Rp08fj8o1a9bMurCV5PpHC8f3UdmyZdWrVy+P6u7Ro4cqVKjgsp5cnTp1su5/+umnev755z2+gnww8PTHleXLl1v3u3bt6nZEvSPHU/4D9ZmWpHXr1jmNRPNk5LF0Nql29dVXW8ubNm1ym0jxprzbFMdEIvyjXLlyTj8WJSYmaujQoU4XL3SUkpKiIUOGuBxt6+69M2PGDHXp0kWzZ88utFzuWTwXXnih1q9fX2jZ3Av+5SrKhfgcfxSW3Lff27KysjRkyBCnH7yvueYap7MvXDmXX7N0dqS945kiu3fv1v/93/8VeGZSQkKChgwZopycnHzPBaL9AID8SOACwDmuR48eWrduXaFJpZycHC1atEgPPvigunfvrnr16qlChQqy2+35ksSOSVtXCVZXPE0c500cuEpU/fXXX04HGG3atPGo7ly1atWy7h84cKBI63pDSkqK0+n/gwcPzncw58hxdG7edYPBxRdfrLi4uCLdHEdbFiYsLEyfffaZNSIwMzNTO3futJ5/9dVX1a5du2K1uyjrOZbdvn17vud37Nhh3W/durXHVyK32Wxq27aty3pyDR8+3OkU/LFjx6p27dq6+eab9d5772nr1q1O81cGkwoVKrgduZfL8Sr259pnOlfe/nM3BYSj9u3bW/ezsrI8no/SGxx/RJACMxoQZ6cNcTxbYtGiRerUqZM+/fRTHTp0SJmZmTp06JA++eQTderUSUuXLpWUf7+Ztz9z5eTk6J577tGwYcOs+cMvvPBCzZw5U/v371dGRoYSExO1cuVKjR492vphbM2aNbr00ku1YMGCAtued/RpUU6pzztlQnF+kCsuY4zuuusuff/999ZjnTt31syZMwud+kQ6d1+zo0mTJjmNuJ85c6Yuu+wyzZkzR8eOHVNmZqb27t2rd955Rx07drTOHPL0PQcA8K/wQDcAAOBazZo1831pjoiIUKVKldSgQQN16tRJAwYMcJsMWbZsmUaOHOl0Sr+n8o5AKUhRTi105Coxlfd037zz+hbFyZMni71ucX355ZdOCRLHaR1c6dSpk9q2bWvN7Tl16lTdc889Pm1jMImJidEHH3ygQYMGOT1+7bXXejxVgSueJhYl54u9uHrPOD5WlHol5+Sjq7ovueQSvfTSS04XOUtOTtZXX32lr776ympfr169NGTIELcXJPQnT0fRpqSkOCU03njjjSJdJNFRID7TBcUuynvB8X3gqi5fqlatmtOypz/MwbuqVKmiuXPnqk+fPtbc13///bduv/32Atfp2rWrOnTooDfffNOpHlcmTpzodPbHyJEj9cYbbzhdxLFMmTK6+OKLdfHFF+v2229Xr169dPz4caWnp2vIkCH666+/XE4rkDehl5aW5vF+P+/ozbx1TZkyRVOmTHFbz8svv+w0LYQnRo4cqU8//dRabtu2rRYuXOhRQtLVa/aUu9fsL40bN9bXX3+t66+/3hqJv2rVqkL/jzfffLOysrKsEdx2u71IZ0wAAHyHEbgAEKQmTpyYb2Tjli1btGLFCn355ZcaM2aM2+Ttt99+q969e7tM3kZHR6tOnTpq3Lix0+nv4eH/+20vECP/vJlcKOj0VF9yPIBu3ry5Lr30UrfrOI7CXblyZbGS7ecyV6em9u3bt0R1FmXEU7ly5az7rkYnOj7mOGetN+qWpDFjxuinn35ymirA0dGjR/Xll1/q+uuvV6tWrVzOpRsIjtuKwpzrn+lcefuvKO8Fx/eBq7p8KW/yOHd0Jvyvffv2Wr16tQYMGFBoOZvNppEjR2rBggVOP2rabLZ8CXlJOnHihNNFSHOTvo7JW1dtcbygXmJioiZOnOiybN4EXlF+gHD8/JcpUybfGSknTpzQrl273N4Ku3ibKw899JDee+89a7lly5b65ZdfXP7/XPHWa5YCO4L1iiuu0B9//KGuXbsWWi48PFxjx461RoTnqlatmtvRygAA/2AELgCUUkePHtVdd92lzMxMSWe/nN9zzz266aab1KlTpwJH8cTExGjfvn3+bKqTvEmRhg0bFnvEYVFHS5bU9u3bnS5EtXPnzmId+EybNk2TJk3yZtOC1j///KO777473+OPP/64rrjiCjVv3rxY9aampno86slxKg9X6zg+VtQEoru6c1155ZW68sortWXLFi1YsEDLli3TihUrdOzYMadyu3fv1oABA/Thhx+6/L8Fo7yf6Ro1ahR7RFdhFx3ytbz9d+bMGY8ulCflny7GnyPy8v4wsGrVKr/FRn7169fXvHnztHHjRs2dO1crV67UkSNHlJGRobp16+rCCy/UbbfdplatWklyntalefPm+X4MkKS5c+c6bZtGjRrldAGrgtxwww2qV6+eNXXS7NmzXY6Od7wInyTt27cv32MF2bt3r3W/SZMmHq1TUo8//rjTqN6mTZtq0aJF+X7MKIyr1+wpx9cs+e91F6RNmzZavny5fv/9d82fP19r165VQkKCjDGqV6+eunXrpltvvVUNGzaUMUZ///23tW7Hjh0D2HIAgCMSuABQSk2bNs0aBWK32zV//nyPLuwU6NNrHS+iJklffPGFR6NYg4Hj6NuSmDFjhl566aWgOVXeV3JycnT77bdbI8zCwsIUHh6u9PR0JScn65ZbbtGKFSuK9X9ISEjwOEl29OhR676rHzYcH3N35XhX7Sis7rzatGmjNm3a6LHHHpMxRps3b9bcuXM1bdo0a+SkMUYPPPCABgwYkO9HiuKOlPLlyNbKlSsrLCzMmtv6kUce0VNPPeWzeL6St/8SEhJUp04dj9bN+77x5L3gLe3atVPVqlV14sQJSdKKFSuKdAo8fOP88893O4/ymTNnnC6+VdAo/U2bNjktX3DBBR63o3PnzlYC9+DBgzp16pTTvKnS2bm/HcXFxalHjx5u605LS9PBgwcLrEc6ezHLcePGedxed55++mmnH0AbNWqkRYsWqW7dukWqp27duqpcubL1nSguLs7jdXft2uW0fN555xUptq906dJFXbp0KbTMjh07rG2FVPB7DgDgf0yhAACl1K+//mrdv/LKKz1K3h47dqzIpyl6W8uWLZ2Wt2zZEqCWFE1mZqY++eQTa7ly5cpOU1O4uzle3Obo0aOaN29eAF6Ff7300ktavHixtTxu3Di98sor1vKaNWv09NNPF6tux6SHO7nzD0v533+S1KJFC+v+tm3brFHt7uQmYF3V4wmbzab27dvr2Wef1d9//63Bgwdbz505c0Zz5szJt07e0a6eJmaPHDlSpLYVhc1mc/q/niuf6bzy9p/jhdnccUywlSlTxuPRi95gs9l09dVXW8unTp3SrFmz/BYfxbdw4UJlZWVZy71793ZZriQjvPOO6M07f6uU/4J9f/zxh0d1r1q1yqn9xb0opafGjRunl156yVpu0KCBFi1apIYNGxarPseLD65du9bjbb/jmTgNGzb0eKR+MMg7RU9B7zkAgP+RwAWAUip3RI3k+dXSFy1a5KvmeKxmzZpOc/ueK4nM+fPnOyXB3nvvvXxzGBd227Vrl+rVq2et763RvMHqjz/+cBp11aNHDz399NPWyNJcr776qn7++eci179w4UKPyu3atctpZNWFF16Yr8wll1xi3U9PT/f4c7Js2TLrwjF56ymqsmXL6p133nEaYetqruS8I+cctwMFyczM1Lp164rdNk9cfvnl1v0FCxY4JXUCwXFUd05OjkfrdOrUyWm+5h9//NHjeI5Jkfbt2/v9qvSjR492Wp4wYUKRLsqEwPjwww+t+5UrV9bNN9/sslzeEd25F0nzhON8p5JUtWrVfGUaN25sTekgSb/88otHc+Tn3Xa7m/u3JF588UU999xz1nLdunW1aNGiEv1Y4vjDR2pqqpYvX+52nV27dik+Pt5a9uVr9jZjjNN3j5YtW3o00hoA4B8kcAGglHI8uPL0QP2///2vr5pTJEOGDLHuz58//5wYsed40FO+fHldc801RVrfbrc7jbBcuHChR8m3c1FiYqJuueUWK4lXtWpVffrpp9acjdOnT7dOTTfG6I477nCa5sATs2bN8mg0ueNFfCTp2muvzVemb9++Thfsevfddz1qw9tvv23dt9vtTsmA4qhSpYpq1KhhLbsaDeaYZJGkP//80229X331lc8vquX4mT5+/Lg++OADn8Zzx3GEYmJiokfrRERE6Morr7SW586dmy/55cqPP/6o3bt3W8uBSOicf/756t+/v7W8bds2Pfnkk8WuLy4uzuMfSVA8ixcvdkr833333QUm/ps1a+a07OmPXomJiU7biJiYmHwXGct1/fXXW/f37t2rn376qdC6s7KyNH36dGu5Xr16RZraoSheffVV/fvf/7aWa9eurcWLF+f7vxSV42uW5NF2K2+ZvHUEs+nTpzt937rvvvsC2BoAQF4kcAGglHI8ZfDHH3+05p8syOTJk51O+wuk+++/37rIUU5OjoYOHepxkiWXP0eXHTx4UAsWLLCWr7vuumKNsBs6dKh1Pzs7Wx9//LHLcj179pTNZrNu59pV5e+9916nNn/44YdOF6eqXr26ZsyYYY02PXz4sO68884ixTh+/Ljb6Re2bt3qdKGbjh07qnPnzvnK1alTx+kg/Ntvv9X8+fMLrXvBggVOp6n36dPH5YVsitJ3//zzj9NFzRyn3cjVoEEDp7keHUfwuXLy5MkSJfI81bVrV3Xr1s1aHjNmTJGmIJDOJoTcbcc85fi/c5xCw51Ro0ZZ91NTU/Xggw8WOhIxKSlJjzzyiLUcHh6u4cOHF62xXvL+++87zZn83//+V0888YRHIykdff3117rgggu0bds2bzfRrSVLljht+4q6XThX7N271+m11atXT2PHji2wfN4pkt58802PfgAcN26c05QJhU21NHLkSKfk7hNPPFHolAKTJk1yasPDDz9c7Dm6C/PWW285jTCvVauWFi1aVOQpa1xp0aKF+vXrZy3PmjWr0Okjdu/e7XQRuHbt2qlXr14lboc/rF+/Xo899pi13KFDB40cOTKALQIA5EUCFwBKKccDsZ07d+qBBx5wedpyZmamxo8fr0cffVSSPLpyta9VrlzZ6SBo8+bNuvjii/Xbb78Vup4xRqtXr9b999+vrl27+rqZlo8++sgpsXTLLbcUq54LLrjA6aBz2rRpRU6uBLtp06bpyy+/tJZHjBjhcoRS79699cQTT1jLP/zwQ5FGiNvtdr311lsaO3asy6Tf+vXr1a9fP6dEv+P8u3m98MILTkn5IUOG6LvvvnNZ9ocffnA61bls2bKaMGGCy7LNmzfXbbfdpsWLFxd6Kv+JEyd02223WWVsNpvL0cKS8w8BS5cudZoT0tGuXbvUq1cv7d+/3yeJlbzeeecda+Tr6dOn1b1793yfHVfi4+P10ksvqVGjRkUeiV0Qx+ksdu/erddff92jaR369evnNCfk119/rREjRricN/TQoUMaMGCA0xXdR48eXeSLKXlL3bp19dlnnzlNH/HKK6+oW7dubret2dnZ+vnnn9WlSxcNGjSoyD+o4awJEybku+BYXj/88IO6d++uffv2STr7WX/vvfcKnUe1YcOGuummm6zlkydP6qqrrnI5zYp0dr8/duxYTZ482XosLCzM6ceGvBo0aOA0InPTpk269dZblZ6enq/s559/rtjYWGu5Xr16uv/++wusu7imTp2qBx54wFquUaOGfv31V69eNOyll16yvhfl5ORo0KBBLudY37dvn6655hqnecdffPHFgH+nevbZZ/NdVC2vTz75RL1797Yu2BYREaFp06Y5nXkCAAg8myltR4YAcI766KOPdNddd1nL06dPL9HootOnT6tp06ZOCY/mzZtr0KBBatq0qdLT07Vjxw7NmTNHe/fulSQNHz5cCxc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1L774YoGfSR06dDDx8fFFtqegv/3kyZNNrVq1nNZds2ZN8+GHH7q0v6NHj7ZvV69ePZe2SU5ONmP+v737jqva+v8H/rrsJQhOXCAoat0DFCeCVax74N5dWq39tLW16+Nota2t1Vb7qa3WrVVx1m1bR504EUURRMEFKEM2yMrvD37km8AduXCBq76ejwcP7jg5OUlOkpt3Ts6ZN0+oUaOGxnXu7u4urF69WsjPz9eYz19//SWb5uDBg4rmX+TIkSM6p1d3zrlx44bg6+srqFSqEuW2trYW5s6dW6LcsbGxwpgxYwQzMzO1dWD8+PFCUlKSXuXPzs4WFi9eLDRo0EDjeqxbt67w888/K9qPNG3L5cuXC/Xq1dN4HPvtt9805ln8eKvkT+nxT5vU1FTh448/FqpVq6Z1XiYmJkK7du2ElStXaszrRTvX7dixQ2jZsmWJ/DUdww1dz4iI6OXAAC4R0UusvAK458+fF6pXr6744nLUqFFCTk6OorxXrlwpmJiY6MyzQYMGws2bN8s9gBsWFqYzcFv8T9N6Ln5Re/XqVY0BIelflSpVhHPnzuksqzR/JX8WFhbCmjVrdOar7qJ20aJFirZT9+7dhczMTJ3zyM/PFyZNmqSo3D4+PkJ6erreAdxz585pDdwW/wsICFBcb5UqSwB3xowZiso9YsQIoaCgwKDlLrJ3716hSpUqitfh+++/rzPP4gHc27dvCw0bNtSZd9OmTcUA3tOnTwVvb2+d0zg5OQmhoaFay1N83efn5wsBAQGKlnf06NFaA6iCoH8A9/bt24Kbm5vidd6zZ08hJSVFbV4FBQWyvIYNG6Zz/lLS9VC/fn21y1r8WPjnn38KNjY2Oss9atQosd6GhIQo2lc7dOggpKamKip7RESE0LhxY8XrsXv37hrXY5Hi2zInJ0cYMmSIovxnzZqlNs/KCOA+ePBA7/Ncly5dNOb3Ip3rtB131R3Dy6OeERHRy8F4n88hIqLnUnBwMPz8/JCeni5+1qhRIwwfPhxubm5IT0/H+fPnsWfPHjx79gwAsHXrVmRnZ2P37t1a8969ezemTp2KgoIC8bMOHTpgwIABqFu3LpKTk3Hy5EkcPHgQ9+/fx/Dhw8v1cfcrV67Az88PycnJ4mcODg7w9/dH+/btUb16daSnpyMqKgpnzpzBxYsXIQiCorwfP36MAQMG4NGjR7Czs8OgQYPg6emJKlWq4MGDB9i2bRvCwsIAAGlpaRgzZgxCQ0NhY2OjM2+VSoU2bdqgY8eOaNy4MRwdHZGdnY2oqCgcPnwY169fB1D4qOzrr7+OBg0awM/PT/F6+eOPP/D5558DABo3bozBgwfD3d0dgiDg6tWr2LRpEzIyMgAAJ0+exLx587Bo0SKteX744YdYt26d+N7U1BT+/v7w8fGBo6MjYmJicODAAZw/fx4nTpzA9OnTFZcXAI4fP47+/fsjMzNT/KxJkybo378/3N3dYWZmhvDwcAQGBuLBgwcAgO3bt0OlUmHbtm16zas8fP311/j5558BAG3atEH//v3h4uKCZ8+eISgoCNu2bUNubi4AIDAwED4+Ppg2bZpBy/DHH39gwoQJyM/PFz9r164d/P394eLigvz8fISGhiIwMBAJCQkAgKVLl8LMzAzfffedonlkZGRgyJAhiIqKgq2tLYYOHQpPT0/Y2NggIiICGzZsQFxcHADg1q1bePfdd7FhwwYEBATg3LlzUKlU6NOnD3r27Inq1avj8ePH2LZtG0JCQgAUPr49ceJEXLhwQXGXB/Pnz8f27dsBAB4eHhg+fDgaNmyItLQ0nDx5Evv370deXh4AYMuWLXBwcMCKFSuUrVQdHjx4gG7duonLDAB16tTByJEj0aRJE+Tk5CA4OBg7duxAWloagMK6/uqrr+LUqVOwsLCQ5adSqfDmm2/i008/BQDs3bsXT548Qc2aNXWWJSEhAX/++af4fsqUKTrX4Y0bN7BixQpkZmaibt26CAgIQNOmTSEIAi5duoRNmzbJzhN+fn7o378//P398fjxY9ja2mLIkCHw9PSEra0tbt++jQ0bNiA2NhYAcOnSJcyZMwdLly7VWQ4fHx+xXgJA/fr1MWjQIDRt2hRWVlaIiorCjh07EB4eDqDw2NW3b1/8+++/irs8mDFjhnie69q1K3r37o06deogPT0dJ06cwJ9//imeIxYvXgxfX1/07dtXloe1tTXc3d0BAI8ePUJ2djYAoEqVKhq3k5OTk6LyqSMIAkaMGIHIyEjxs0aNGqFfv35o3Lgx7OzskJmZibi4OFy7dg0nTpyQnRN1eZ7PdT/++KN43HVzc8PgwYPRqFEjqFQqREZGIiYmRpa+ouoZERG9oCo1fExERJXK0C1ws7OzSzyy+MUXXwi5ubkl0oaFhQlNmzaVpV2xYoXGvJOSkmQtrszMzDS2mLl8+bL4mL20FaghW+CmpKQI7u7usvK/+eabWh/ZjY6OFj744AMhMDBQ7ffSVkNF5e7Vq5cQGxtbIm1ubq4wZcoU2fz/97//aS1zmzZthDlz5gjR0dFa0wUGBspaUTZs2FBrq8HirZJMTEwEExMTYfHixWof/4yKipItq6Wlpdb1durUKdmj1c7OzsL58+fVpt28ebNgYWFRYttra4H2+PFjWd2ysrISVq9erbaVamZmpvD222/LlnfDhg0a89ZXaVvgmpiYCFZWVsLGjRvVpr98+bLg6OgoW4eGfDT35s2bslaUTk5Owt69e9Wmffr0qTBo0CAxrUqlEk6cOKExb2kL3KJ64O3trbYrkqdPn8q67VCpVMJ7770nABBq166ttvVefn5+iX1JU9kFQf26ByB89tlnatfppUuXSjwu/88//2jMX2kL3IKCAsHPz0+W75QpU9R2S/Lo0SOhS5cusrSzZ89Wm29cXJxgbm4uplu8eLHGMkgtWbJEtk7u3bunNp20DEXb8+2331bbEj8kJETWFYerq6swePBgAf+/haemOtCmTRtxGnNz8xLdaUhlZmYKzZs3l5X9m2++Udu6Pi8vT5g/f75sGb788kuNeUu3ZVE9cXR0FP766y+16Q8dOiQevwAInp6eGvMWBPXdixha8eP7l19+qfV8kJubK+zfv1/49NNPNaZ5Uc51RX/z589X+ztHqjzrGRERvRwYwCUieokZOoD7888/y/J77733tKa/f/++rD89JycnjY/Tz5kzR68LuBs3bpToS8+QAdyPP/5YUTBEH8Uf+2zXrp3w7Nkzjemzs7MFV1dXMb22R1YFQRCysrIUl+XAgQOysvz5558a06q7qNUV9CneV+aqVas0pu3evbssGBMcHKw1799//71EebQFcCdPniy7qD506JDW/AVBEIYNGyYLsul6LF6p0gZwAQjbt2/Xmvdvv/0mS//3338bpMyCIAg9e/YU87WxsdHZp2Rubq7QsWNHcZquXbtqTCsNUhUFWbQ9Fn/lypUS68bCwkJrmTIyMmRB/DFjxmhMq27dT506VevyXr9+XXY8atu2rca0SgO4+/fvl5VhyJAhWrvGSE5Olj26bWZmprE/7uHDh4vpmjVrpnXZikiDU/7+/hrTFV93w4cP15qvNDBc9Ofu7i6kpaVpnObSpUuy9Nr6Yy0eKNOWtsiHH34opre1tRWSk5PVppNuy6J1rqsLgE8//VQ2TUREhMa0FRHA/e9//yvOo02bNgbJ80U61yk995dnPSMiopcDA7hERC8xQwdwpa1vnZ2dhfT0dJ3TFA/6qmtVm5+fL2vB1rJlS0UBs1mzZpVLADc1NVVwcHCQXdTqan2jRPGL2rNnz+qcZt68eWJ6Kysrg5SjiDRwqi1AVfyitmnTpor6WJVekL/xxhtq04SHh8vynj59uqKye3p6KgrgxsbGylq8vfXWW4ryf/TokayV4r59+xRNp0tpA7i9e/fWmXdGRoasleyCBQsMUubiAdOvv/5a0XQXLlyQTXf9+nW16YoHcDW1YJeSBhMBCDNmzNA5zTvvvCMLEGpSfN07OTkpCqx89NFHsukuXbqkNp3SAO5rr70mprO2ttYYjJUqHvSdM2eO2nTFBzM7c+aM1nzPnj0rS79jxw6NaYsHNB89eqQ178ePH5cY3Exb/kWk56PXX39dbZrs7GyhZs2aeu1HglDYmlJ683H58uVq0xUP4Co5vkRHR8um2bRpk8a0FRHAffPNN8V5jBgxwiB5vijnulq1aikKFpd3PSMiopeDss69iIiIdLh79y5u3rwpvp84cSJsbW11Tjdp0iRZuv3795dIExoaiocPH4rvJ0+erKh/yjfeeENnmtI4cuQIUlJSxPezZs0yeN90Hh4e8Pb21pmuU6dO4uvs7GxER0cbrAy9evUSX1+8eFHxdBMnToRKpdKZTlr2oj4Oizt8+LDs/euvv66oDErT7dixAzk5OeL7999/X9F0derUka2fv//+W9F05WXy5Mk609jY2KBVq1bie03rXF+bN28WX1tYWCjuf9jT0xPNmjUT3ytZh1WqVMGQIUN0pmvXrp3s/fjx4/Wa5u7du2KfwbqMHDkSDg4OOtO99dZbsvd79+5VlL86z549wz///CO+HzRoEOrWratzutdeew0NGzYU36s73gKF+35RP6sA8Pvvv2vNV/p9zZo1MXDgQJ1lKZpPnTp1tKapWbMm6tWrJ763t7fHoEGDdOYt3Z63bt1Sm+bvv//GkydPxPdK939ra2uMGDFClo8SSvZTFxcX1K5dW3xvqP20tKR9zV66dEnxfqHU83yuGzVqFKysrHSmq+h6RkRELyYGcImIyCDOnz8ve1984BVNbG1t4ePjozEfoOQFlTS9Nk2aNJFdCBvKqVOnxNempqYYPHiwweeh5IIWQImgjT6Dx+ji7Owsvn706JHi6aQX2tpIy66p3NJt7+DggLZt2yrKW2kdkW5LNzc3NG3aVNF0AODl5SW+VldvK5Ih17m+pOuwa9eusLe3VzytvuuwXbt2im6W1KpVS3xtbm6uqN5IjxWCIMhu0mjTp08fRekaNWokC4rqEygq7sqVK7IbD0qPtyqVCq+99pr4/tq1a8jKylKbTnoDLDAwUBwErbj09HQEBgaK7ydOnAhzc3NF5enYsaOidNLtqbQOSLenprourbtWVlbw9fVVVB5A/7prbm6O9u3bK8q7PPbT0ip+Y2PUqFGyG6pl9Tyf6zp37qwoXUXWMyIienExgEtERAYREREhe9+6dWvF00pbBT569AiZmZmy76OiomTvX3nlFcV5N2/eXHFapaQtjZs2baqopbG+lAae7ezsZO8zMjJ0ThMbG4uffvoJAQEBaNasGapVqwYLCwuoVCrZ35tvvilOo8/FcmnKrqnc0m0vba2pS+PGjRUFkUJCQsTX+tYVaVDJkAGN0jDkOtdXRa5Dpcsp3SednJwU1YXi+7HS9dOyZUtF6Yqn1dQqVAlDHW/z8vJKHF+LTJ48WVxvGRkZ2Lp1q9p0W7duRXp6uvhenycfSrM9pXVG6TSatqW07jZu3BgWFhaK8i5ejsePH+tsmaq0HgLls5+W1tChQ1GjRg3x/a5du+Dq6go/Pz8sWrQI586dk91M0NfzfK5r1KiRonQVWc+IiOjFZdjnPYmI6KX19OlT8bWFhYWiR4qLFL8gf/r0qeyxTekFlbW1NSwtLRXn7ejoqDitUomJieJracsdQ1LyWKY6giBo/C4zMxNffPEFli9fjry8PL3yzc7OVpy2NGXXVG7pttdnW5qYmMDBwQEJCQla00m35b59+xR1/aCOtP5XBkOuc31kZGTg2bNn4vvly5dj+fLlpcpLyToszXKWx74kVbNmTcV5SgNhZakzxafVpwzqjrea0g0aNAg7duwAAKxevVoW6Coi7T6he/fu8PDwUFyWitqemraldP+/fv16qfd/oPBYJd2+xZV3PSwvdnZ22Lp1KwYNGiQG6vPz83Hs2DEcO3YMQGE3C127dsWQIUMwatQoVK1aVXH+z/O5TunTBhVZz4iI6MXFFrhERGQQ0hZY0uCrEsVbvknzAiALEOkTvC1NeiWkjxIXbxVkrLKystC3b18sXbq0xAWtiYkJqlevjnr16sHd3V380ycoVF7Ke9sb6jHc4q3GXxaGfIz5eV2H1tbWitNKj3XFj3P6KD6tPsdcXcdbKWm/vefPn0doaKjs+xs3bsge61YX4DVmrL/K+Pr64vLlyxg2bBhMTU1LfJ+ZmYm//voL06ZNg4uLC77//vtKCzxX5LlOad/3rGdERGQIbIFLREQGIQ1k6nuBUfxRyOJBUWkrF32DHqmpqXqlV0JavrIEYSrSggULcPLkSfF98+bN8e6778LHxwdubm5qH+1du3YtpkyZUpHFLEG67TX1wamJkm1vY2MjpnN0dISTk5N+BXzJFQ8c1qhRQ68+cKWkA1U9T7KyshTfyJEe68py86f4tJmZmYqfetB1vJUqGszszp07AApb4S5dulT8Xtr6tmrVqhg+fLiiMhgLaf21s7NT3D2DOoYeyNLYeHh4YMeOHYiLi8PBgwdx4sQJnDlzBnfv3pWlS01Nxccff4wbN25g3bp1FV5OYzzXsZ4REZEh8AxAREQGIX28PScnBykpKYoDCtLRmYvnBcgfO87Ly0NsbKzirgvKo2/SatWqia9jY2MNnr+h5ebm4ueffxbf+/r64uDBgzpbqFb24DmAfNs/ePBA8XQpKSmKAr7Vq1cXA7gBAQH47bff9C/kS6xq1aowNTVFfn4+gMLR1T/99NNKLlXFevLkieJgbHx8vPi6LN27FJ/2yZMnio+Juo63UkWDmRVt040bN2LRokWwsLBATk4ONm3aJKYdN25cqR+HryzVq1cXX3fu3BlHjhypxNI8H2rXro0pU6aIAc8HDx7gwIEDWL9+PYKCgsR069evR0BAAPr161dhZTPWcx3rGRERGQK7UCAiIoMo3u+hdNAOXa5duya+rlu3bolWfcUH6Lly5YqifLOzs2UDjhlKixYtxNe3bt2q9EFmdLl48aKsNerChQsVdS9QvGVVZZBu+8jISMWtcIODgxWla9q0qfj6xo0b+hWOoFKp0KRJE/H9y7gOr1+/rjittAsC6XrTl6GOt+bm5mjYsKHW9NLBzBITE7Fnzx4AwO7du2V9TOszeJmxkO7/5XGueBnUr18fU6dOxblz5/DTTz/JvpMG+CuCsZ7rWM+IiMgQGMAlIiKD6NSpk+z9oUOHFE2XmZmJEydOaMwHALy8vGBi8n+nrN27dyvK+8CBA7I+VA2le/fu4uv8/HwxoGGsHj16JHuvdMT6o0ePlkdx9OLt7S2+LigowJ9//qloup07dypK17NnT/F1UFCQzkHPqCTpOjx8+LDegwY975S2prtz5w4iIyPF956enqWeZ7t27WQj2Ss93gLAwYMHxdetWrXS2Ydv0WBmRYq6TVi9erX4maenp+LjijGR1t2HDx8qvvFjLKTdARQUFFRiSQrNnDlTVg8qOlhprOe6572eERGRcWAAl4iIDKJhw4Zo3ry5+H7Dhg2K+sJdv369rB/Z/v37l0hTrVo1+Pv7i+83b96Me/fuac23oKAA33zzjZKi661Pnz6yx45/+OEH8RFyY1R8MBklo2z/9ddfCAsLK68iKda3b19Zv7SLFi3Sua5jYmKwdu1aRfkPHz5c7FMwPz8f33//fekL+5IaNWqU+DoxMRGrVq2qxNJUvMDAQEX9La9cuVL2XhoU1ZelpSVeffVV8f2ff/6pqDuXQ4cOyVobqjveqvP222+Lr//55x/8+++/sqDX89j6Fig8lletWlV8/+2331ZeYUpB2nVHSkpKJZbk/0hblufm5lbovI31XPe81zMiIjIODOASEZHBvPPOO+LrmJgYzJkzR2v6hw8fytI4OTnJgkFSM2fOFF9nZ2djzJgxWgPEn3/+OS5fvqy06HqxtbXF1KlTxffBwcH4/PPPy2VehtCgQQPZ+/3792tN//TpU9nyVSZLS0u89dZb4vvQ0FDMmjVLY/pnz55h7Nixiru1cHV1xejRo8X3S5YswV9//aVXGQVBQE5Ojl7TvEi6du2Kbt26ie9nz56t1yP9QGHf1sZ8E0SbxMREfPbZZ1rT3Lx5E8uWLRPft23bFu3bty/TfKXH26ysLMycObNEAEsqNTUV77//vvjezMxMtm9p4+fnB3d3dwCF9X3kyJFii09bW1vZPvQ8qVKlCt59913xfWBgINasWaN3PkoCheXB1dVVfC3tnsOQoqOjFacVBEHWpYi0fBXBWM91z3s9IyIi48AALhERGczrr78ua4X7ww8/YP78+WoDM+Hh4ejTp4/skfWvv/5a4yA4ffr0wbBhw8T3Z8+ehZeXFw4ePCh7ZDskJAQBAQH49ttvYWJiIut7zpA+++wzNG7cWHy/aNEivP3221oHQ3nw4AE++ugjbN++vVzKpEmHDh1krVg//PBDXL16VW3aGzduoHv37oiKipJ1W1GZvvjiC7i4uIjvf/zxRwwaNEjWF3J+fj7++usvdOzYESdOnECtWrVky6zNd999Jw4AlZeXhwEDBuCHH37QebEcGxuL5cuXo2nTpor7ZX5RrVixQmwNmJaWhu7du2PdunU6g7JRUVH45ptv4OrqKhvg63liYmKC//3vf5gzZ47a5Q0ODkbfvn1l9ckQLb379u2LXr16ie937NiBqVOnIisrq0Ta2NhY9O/fH+Hh4eJns2bNQp06dRTNq2gwsyKPHz8WX48cORJVqlQpzSIYhdmzZ8v6NX/jjTfwySef6GxVnZSUhLVr16JDhw6V1o2OtMuhu3fvYunSpQbvwmTy5Mno3r07tm3bpvWmaV5eHmbNmiVrzTp48GCDlkUXYz7XPc/1jIiIjINZZReAiIiMx+zZs7FgwQK9phk2bBgWLVoEoLC15MaNG9G9e3exW4R58+Zh8+bNCAgIQMOGDZGRkYGgoCDs2bNHFtAYPHiw7DFddVauXInbt2+Lg/DcuHED/fr1g6WlJWrWrInk5GTZIFdffPEF7t27h1u3bgEATE1N9Vo2bezs7BAYGAhfX188ffpULN+2bdvw2muvoV27dqhWrRoyMzMRFRWFoKAgnD17FoIgKH6831DMzMzw0UcfiSPJx8fHw8vLC4MHD0bXrl1RpUoVxMfH4+TJkzh8+DDy8/Nhb2+PadOmidu2Mtna2mLnzp3w8/MTHxPeu3cv9u7dCzs7Ozg6OiI+Pl6sTyYmJti8eTNef/11JCUlAdC+7WvXro2dO3fC398fqampyMnJwaxZs/Dtt9+iT58+aNOmDZycnJCfn4/k5GRERETgypUrCA4O1tri8WXSvHlzbNy4ESNHjkROTg5SU1MxefJk/Pe//0WfPn3QokULVK1aFc+ePUNSUhJu3bqFS5cuvRAD+nzxxRf48ssv8dVXXyEwMBABAQFwdXVFWloaTp06hX379skeJZ86dSr8/PzKPF+VSoU1a9bAy8sLcXFxAAqPQQcOHMDIkSPRpEkT5Obm4sqVK9ixY4csUOTl5YX58+frNb/Jkydjzpw5JR6Lf/PNN8u8LJXJ1tYWu3fvRvfu3REbGwtBELBo0SL88ssv6NOnDzp06IDq1asDAJKTk3Hnzh1cvXoVFy9erPT+ngcOHIjq1auLN0I/+OAD8YaXtI/kqVOnlrqlqSAIOHXqFE6dOgVbW1t4e3ujQ4cOqFOnDuzt7ZGRkYFbt25h7969sq6NWrVqhcmTJ5dtAfVkzOe657meERGRcWAAl4iIRE+ePMGTJ0/0mkbaEgsofDT46NGj6Nevn3hRefv2bXz99dca8xg1ahQ2bNigc15OTk74559/MGnSJNlAPM+ePcODBw/E96ampliwYAFmz56NMWPGiJ/b29srXi4l2rRpgzNnzqB///5iv5IpKSnYsmULtmzZYtB5ldVHH32EoKAgcRCw3NxcbN++XW1rYAcHB2zbtk1Rn5oVpX379jh69CjGjRsnBuQBID09XdaHcrVq1bB582b4+fnJBrDTte29vb0RFBSEwYMHIyIiAgCQkJCAzZs3Y/PmzTrLZ8ibA8+rwYMH49ixYxg+fLgYUHz48KFssCttjKXFt77mzp2LsLAwbN++HeHh4Vpvgo0aNQo///yzweZdv359nDp1Cr1790ZUVBSAwoGclixZonEaHx8f/Pnnn7IAnxJFg5nt2LFD/Kx58+ZqB5583jRq1AiXLl3C0KFDcf78eQCFLcl37NghW15NKmv/t7a2xsaNGxEQECAeBzMzM0v06Vq0P5ZVRkYG/vnnH/zzzz9a07Vu3RqHDh2SDbJWUYz5XPe81jMiIjIOz+cvZSIiMmpeXl4IDw/HzJkztQbOWrVqhV27dmHLli2KL/Rq1KiBAwcO4MCBAxg1ahRcXV1hZWUFJycntG7dGh9++CFCQ0PxySefQKVSiS0wAcgGETGUZs2a4caNG/j+++9L9L9XXIMGDfDRRx+hT58+Bi+HLqampti1axe++eYbjV0LWFhYYOjQoQgJCamUMurSvn17XL16FStXrkSvXr3g7OwMCwsLODs7o3Pnzli8eDHCwsLQp08fCIIgtowGlG37Zs2aITQ0FL/++iteeeUVnelfeeUVfPjhhwgODoanp2dZFu2F0aVLF0RGRuLbb79Fw4YNtaY1MTFB+/btMWfOHNy+fRs1a9asoFIalomJCQIDA/HTTz+hbt26atPUqVMHq1atwpYtWwwehGnUqBFCQ0Mxd+5c1KhRQ2M6d3d3/P777zh69Gipb2ZNmjRJ9v55b30rVadOHZw9exZbt26Fp6cnVCqV1vRubm6YNm0aTp06hYCAgAoqZUn+/v64ceMGvvjiC3Tp0gU1atTQOzivzYIFCzB9+nR4eHjoTOvm5obvv/8eFy9eFLulqWjGfq57XusZERFVPpXAZ/+IiKgc5ebm4syZM4iMjERCQgKsrKxQq1YteHt7V8gAJ3Xq1BFb1yxYsKDcBxu7efMmgoOD8eTJE2RkZMDOzg7169dHq1atZH3mVqasrCycOXMGYWFhSE1NRbVq1VCnTh107969XILclSEiIkI2Gvrp06fRpUsXvfJ49OgRgoKC8PjxYzx9+hQWFhZwdHSEu7s7WrRooTVYRoUiIyNx+fJlxMfHIyUlRbzZ0rhxY7FbhefNunXrZI+GS39KFxQU4NSpU4iMjMSTJ0/g5OSEpk2bolu3bhXSwrigoAAXLlxAWFgYnjx5AjMzM9SqVQtt27aV9U9eWt9++634eLqlpSViYmIU9zX9vElISMCZM2cQFxeHpKQkmJqawsHBAQ0bNkTz5s01ButfZAkJCbh+/Tru3r2LpKQkPHv2DLa2tqhduzbatm1bbn3Ol9bzcK5jPSMiIqUYwCUiohfW1atX0bZtW/H9oUOH4O/vX4kloory448/4v333wdQ2EIyNTUVtra2lVwqehFoC+C+yARBQJMmTXD79m0AwOjRo/HHH39UcqmIiIiIXg7sQoGIiF5Y0n537ezs0LVr10osDVWUjIwM/PTTT+L7bt26MXhLVEb//POPGLwFoHPQSSIiIiIyHAZwiYjouZGZmYlr164pSvvdd9/JBi0ZM2YM7OzsyqtoVM6ioqJKDJinTnZ2NsaPH4/o6GjxMwaaiMruq6++El83b94cPXr0qMTSEBEREb1cGMAlIqLnRmpqKtq0aYPXXnsNmzdvxsOHD2XfZ2Vl4a+//kLfvn0xe/Zs8XNHR0fMnTu3ootLBnT+/Hk0bNgQr7/+Og4fPozk5GTZ9wkJCdi4cSPat2+P3bt3i5937twZI0eOrODSEr04srKyMGvWLJw6dUr8rLz7EiciIiIiObPKLgAREZE+BEHAoUOHcOjQIQBAlSpV4OjoiOzsbCQmJiI/P1+W3srKChs3bkSdOnUqo7hkQFlZWVizZg3WrFkDAKhWrRrs7OyQlpaGp0+fluiLtEGDBti0aVOFDB5F9CKZM2cO9u7di2fPnuHevXvIysoSv+NNESIiIqKKxwAuERE9N0xNTWFiYoKCggLxs7S0NKSlpalN/8orr+D333+Ht7d3RRWRyom5uXmJzxITE5GYmKg2fd++fbF69Wo4OzuXd9GIXjj3799HSEhIic/r1auHzZs386YIERERUQVjAJeIiJ4bNWrUwKNHj7Bv3z6cPn0aoaGhePDgAVJTU5Gfn4+qVauidu3a6Ny5M/r27YuBAwcy0PCCGDZsGEJDQ3HgwAGcO3cOERERiImJQXp6OkxNTeHk5IQGDRqgR48eGD58ODw9PSu7yEQvBEtLS7i4uGDAgAH45JNPUL169couEhEREdFLRyUUf96QiIiIiIiIiIiIiIwCmyURERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEgxgEtERERERERERERkpBjAJSIiIiIiIiIiIjJSDOASERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEgxgEtERERERERERERkpBjAJSIiIiIiIiIiIjJSDOASERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEgxgEtERERERERERERkpBjAJSIiIiIiIiIiIjJSDOASERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEgxgEtERERERERERERkpBjAJSIiIiIiIiIiIjJSDOASERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEgxgEtERERERERERERkpBjAJSIiIiIiIiIiIjJSDOASERERERERERERGSkGcImIiIiIiIiIiIiMFAO4REREREREREREREaKAVwiIiIiIiIiIiIiI8UALhEREREREREREZGRYgCXiIiIiIiIiIiIyEiZVXYBiIiIiIiIiCqTIAjIzMys7GIYHRsbG6hUqsouBlEJ3GdL4v76YmMAl4gIgKurK+7duwcAiIqKgqura+UWSAMl5fTx8cG///4LADh+/Dh8fHwqsIRkjMLDw7Fs2TKcOHEC9+/fR3p6uvidMdd30m3evHmYP38+AGDu3LmYN29e5RaIyEisW7cOkydPBgBMnDgR69atq9wCkdHLzMyEnZ1dZRfD6KSnp8PW1rayi2FQlfW7f9KkSVi/fj0AYO3atZg0aVKFzPdFxX22pBdxf6X/wy4USDRq1CioVCrxb8mSJZVdJCIiKqP9+/ejTZs2+OWXX3Dz5k1Z8JaIiIjI2Pj4+IjXpPremJw3b544LRsxEJW/suyvpB8GcAkAkJKSgj///FP2WdHdQSJNpD+QeLB++URHR4vbny04jVNGRgYmTpyI7OxsAICzszMCAgLwzjvvYPr06Zg+fTrs7e0ruZRUZN26deI+xVY5RP+H+wZVtMePHyM9Pb1MfzNnzgQAzJw5s8x5VfQ8Hj9+XJmrn6hUXF1dcevWLZ31e+7cueI0c+fO1Zn+1q1b4rWOsc6DXg7sQoEAAIGBgeIFfpFr167h6tWraNOmTeUUioiIymTfvn1ISkoCADRv3hwXL16EtbV1JZeKiIjIuNna2pb5MWQLCwvxf3k90lwR8yB6Hri6uuLkyZOoX7++1nRff/212PXUwoUL8dlnn2lN/+DBA7z22muIjo6Gm5sbTpw4YXTzmDt3rviaXmxsgUsA5K1trays1H5ORM+HEydOQBAECILAR8decleuXBFfjx49msFbInppTJo0STwXsv9bIpKKjo4Wjw98iuzFcPjwYUVBz88//xyA8sCqj48P7t69q1dgtaLn8dFHH2lNTy8OBnAJd+7cwZkzZwAAJiYmWLRokfjdH3/8gby8vMoqGhERlcHTp0/F187OzpVYEiIiIiKi8lGvXj2t37+owVtd86AXCwO4JGtl6+Pjg6lTp8LJyQkA8OTJExw6dKiyikZERGWQm5srvjYx4SmfiIiIiF4uxhhYZfCWSoNXcy85QRCwceNG8f348eNhYWGBESNGiJ9p6kbhyZMnMDc3h0qlgqmpKR49eqR4vu7u7uJgFPv27dOa9ujRo5g6dSqaN28OJycnWFpaok6dOujTpw9+/vlnZGVl6Zxf0bxUKpX4WUhICN577z20aNECTk5OUKlUGDx4cIlpL1++jG+++Qb9+/eHm5sb7OzsYGFhgVq1aqFz5874/PPPcf/+fcXLDhSu9y1btqBv375wdnaGlZUVXFxc0L9/f+zYsQOCIACQj+h44sQJnfnm5uZi48aNGDFiBNzc3FClShXY2tqiYcOGGD16NHbv3i3mXRZF5ZL2tTN//nzZetY22EhWVhb27NmDmTNnomvXrqhVqxYsLCxgZ2cHV1dXDB06FGvWrEFOTo7iMl28eBEzZsxAu3bt4OjoCHNzczg5OcHDwwNdu3bFBx98gCNHjiiqL7osXLhQXD5zc3PZPlRa+fn5+P333+Hr64uaNWvC2toabm5uGDlyJP7++2+98lJab3Jzc7Fp0yYMHTpUrNtF+1erVq3Qr18//PDDDwgNDZVNVzSYTMOGDcXP7t27p3b7S/c5KUPuV66uruK8oqOjAQAPHz7Ef//7X7Ru3RpVq1aFra0tmjZtinfffRf37t1TlG+R/Px8BAYGYsKECWjSpIlYv6pVq4ZOnTrhvffew9GjRxXtWxcvXsT777+PNm3aoEaNGrCwsEDt2rXRo0cPLFq0SNZitrSkgwtKj9+TJ08usW2k9UPdoIRZWVlYvXo1evfujQYNGsDCwgIqlQpXr14tMd/09HQsW7YMffr0Qb169WBlZQVHR0e0aNECM2bMwPnz5xWVX13duXz5Mt588014eHjA1tYW9vb26NatGzZs2KB2vR89ehTDhg2Du7s7rKysULt2bQwZMgSnTp1SthJLISgoCDNmzEDz5s3h6OgIKysr1KtXD/7+/vj555+RkZGhcdpJkyZBpVJh8uTJ4mfr169Xuz8p6RYlLy8PGzZsQK9evVC3bl1YWlrC2dkZgwcPxv79+/VeNkPUW3X76Z07d/D555+jbdu2qFGjBkxMTAzS776h6qJUamoqli9fjgEDBsDV1VV2vPTz88P8+fNx48YNRXkdOnQIb7/9Nlq0aIFq1arB3NwcVatWRbt27fD2229j7969ap9+0ncgLyUDTWpKc/z4cYwZMwbu7u6wtrZG9erV0bVrVyxbtqzEmAmaPHnyBGvXrsXEiRPRtm1bODk5icvatGlTTJ48GUeOHNGaR1n2DX3XlyAI2L59O0aPHg13d3fY2dnBzs4O7u7uGDNmjOz3mTbqzsFJSUlYtGgRPD09Ub16dfEc//rrr5c4x2pS2nM2EZWk7pykzcGDBzFs2DDxnFK3bl34+flh3bp14s3youOVSqXSq9uWjIwM/PLLL+I1kaWlJerXr4/Ro0eLT8lS6RljYJXBW+2OHDmCKVOmwMPDA/b29rC2toaLiwuGDBmCtWvXyhqoFBcXFyfuh7rWab169RSnlR4z9Il7GZxAL7Xjx48LAAQAgrW1tZCamioIgiCcOXNG/NzCwkJITExUO72/v7+Y7vvvv1c0T2ne1apVE3JyctSmu3//vuDj4yOm1fRXp04d4eTJk1rnKU0vCIIwd+5cwdTUtERegwYNkk3n6empc/4ABHNzc2HRokWKlj8pKUnncg0cOFBISUkRevToIX52/PhxrfkeP35ccHd311nWTp06CQ8fPlRUVk2k5dL1N3HiRNm0QUFBgp2dnaJpXV1dhStXrmgtS25urvDWW28pLs/nn3+uNh8XFxcxTVRUlNo0BQUFwsyZM2X7zP79+0uzCmUePnwotG/fXmu533jjDeHZs2eKyqmk3oSHhwvNmjVTvN5u374tTrt27VrF06k7zRh6vyq+Tnbv3i04ODhozFef7Xby5EnBw8NDUXlnz56tMZ+kpCRh2LBhOvOoWrWqsH37dkVl02Tu3LmKt420fkinmzt3rnDz5k2hefPmaqcLDg6WzXPfvn1C7dq1dc5vzJgxQkZGhtbyF687CxcuVHu8LvqbMGGCUFBQIAiCIKSnpwsDBgzQmFalUglLliwp0/otLj09XRg5cqTOZXd2dhYOHjyoNo+JEycq3mY9evSQTVt8uz18+FDo3Lmz1jwmT54s5Ofn61w2Q9bb4vvpb7/9JlhZWZXIq3Xr1kpXvVqGrItFVqxYITg6OiraPocOHdKYT2hoqNChQwdF+YwcObLE9NJjb/FzqzpRUVFiehcXF0VpcnJyhKlTp2otm4eHh3Djxg2t8/7pp5+07rfSP19fXyEhIUFtPmXZN/RZXxEREULbtm11zqN9+/ZCZGSk1ryKn4NPnz4t1K1bV2OepqamwsqVK7XmWZZzNimXnp4ursP09PQy5zdr1iwBgDBr1iwDlK5i52HodaEv6X40d+5cvaaVnheLHxeKKPk9LQiCkJ2dLYwYMULr/ubt7S08evRIdrxau3at2vyKp7l586bOfXvOnDl6Lf/LRFc9Xbhwofj9woULdeZ3//59wc3NTQAguLm5Cffv39c5jTHM43neX6UeP34s+Pn56TzHNW7cWLh48aLGfJo0aaLzfBgREaH43Hn37l0xTaNGjUq9fIZgBnqpSVtnDR48GFWqVAEAdO7cGY0aNUJkZCRycnKwdetWvPPOOyWmHzduHA4fPgwA2Lx5M2bNmqVznps3bxZfjxw5Eubm5iXShIWFwc/PD7GxsQAKW2S1adMGzZs3h42NDR49eoSTJ08iLS0NMTExePXVV3Ho0CH07NlT5/y///57seWou7s7vLy8YGNjg+jo6BJlKWoBaGlpiebNm6NRo0ZwcHCAIAiIjY3F+fPnkZCQgNzcXMyePRsA8PHHH2ucd3Z2Nvr06YOLFy+Kn9WvXx9du3aFjY0NwsPDcfbsWezdu1fW2kSX7du3Y+zYseLdKCsrK3Tq1Amurq4wNTVFREQEzp07h7y8PAQFBcHb2xsXL15ErVq1FM9DasiQIWjRogUuXLggLounpye8vLxKpO3UqZPs/dOnT5Geng4AqFmzJpo3b4569erB1tYWmZmZiIyMxIULF5CXl4fo6Gj06NEDV65cQaNGjdSWZdasWVi5cqX4vm7duvDy8kL16tUhCAISExNx8+ZNhIeHl2pZi+Tm5mLSpEn4448/AABVq1bFvn370LVr1zLlm5SUBD8/P1n5GjduDC8vL5ibm+Pq1au4evUqfv/9d9jZ2ZVpXkXS0tLQq1cvPHjwAEDho/Vt27ZFs2bNYGdnh8zMTDx69AghISFISEgoMX2zZs0wffp0pKWlYcOGDQCAKlWqYMKECYrmb+j9Suro0aN4++23kZ+fjwYNGsDb2xv29vaIiorCiRMnkJeXh6ysLIwYMQKhoaGyVsTFbd26FRMmTJDd5fXw8EC7du3g4OCAlJQU3LhxAzdu3EBBQYHGVmlxcXHw9fVFWFiY+FmzZs3Qpk0bVKlSBU+ePMHp06eRkJCA5ORkjBgxAhs3bsTYsWMVLXNxXl5emD59urg+bt26BQDw8/ND06ZNZWnr1q2rNo/ExET4+/vj/v37sLKyQrdu3eDi4oK0tDQEBQXJ0m7btg1jx45Ffn4+AMDU1BRdu3ZFo0aNkJ6ejlOnTiEmJgZAYb/qUVFROHbsmGzATE1WrFghtjZo3749WrZsCUEQcOrUKdy9excAsGHDBjRp0gSffPIJhg0bhiNHjsDCwgJdu3aFm5sbUlNTcfToUSQmJkIQBHzwwQfw9PQs874LAJmZmfD19cWFCxfEz+rUqYNu3brBzs4OkZGROH36NPLz8xEbG4uBAwdiy5YtGD58uCyfXr16wc7ODrdu3cLRo0cBAE2bNoWfn1+JeTZu3FhjedLT0+Hv74/Q0FDY2NigW7duqF+/PtLS0nD8+HE8efIEALB27Vo0adJE3L/UKc96u337dnGfrlu3Ljp37gwHBwfExMQgKSlJ5/SalEddnDlzJpYvXy6+NzU1haenJxo3bgwrKyvEx8fj6tWrYgsuTceBEydOYODAgUhLSxM/a9CgAby8vODk5ISMjAyEh4cjJCQEubm5ilu5Gtrs2bPx66+/AgBatGiBtm3bQqVS4cqVK2LLzoiICPj6+uLs2bNwc3NTm09MTIy4Hdzc3NCsWTPUqFEDVlZWSE5OxvXr18UWy8eOHUOvXr0QFBQES0tLWT6G2je0CQsLQ48ePRAfHy9+1rJlS7Rp0wYqlQrBwcG4fv06gMKnAbp06YKTJ0/Cw8NDZ96hoaH49NNPkZ6ejpo1a6Jbt26oVq0aHj16hGPHjiErKwv5+fmYOnUqWrRoAW9v7xJ5lPWcLbVu3TrZ70vBAE9lEb2oBEHAiBEjsHfvXvGzGjVqwMfHBw4ODoiOjsa///6Lc+fOYejQoRqvVTSJiYlBr169EBMTg6pVq6Jbt26oXbs2EhIScOzYMaSkpAAAvvzyS7zyyisYOXKkQZfvRWeMrWLZ8lazx48fo0uXLrhz5474mbu7Ozp27AhLS0vcvHlTfILq9u3b6NmzJw4fPowuXbqUyMvHx0e8tj5+/LjafVPdU6pK0lb6AOGVGDymSpaRkSFUqVJFvJtw4MAB2ffz5s0Tv+vYsaPGPKStKUNDQ7XOMycnR6hevbqY/uzZs2rzlN6J7NWrlxAeHl4iXUpKiqyViLOzs5CcnKx2vpDcWTEzMxMcHByE3bt3l0iXnZ0tez9t2jThwIEDQmZmptp88/LyhLVr1wq2trYCUNhi8O7duxqX/9NPP5W1uFi+fLnYcqxIaGiouPyWlpayVhzqhIaGCjY2NmK6//znP2pbTN+5c0fo2rWrmK5v374ay6lU8ZZfSgQFBQmfffaZcP36dY1pHj9+LIwfP17M28/PT226+Ph4wczMTFyf69atK7E+i8TExAjLli0Tfv/9d7Xfa7sTn5GRIWttXrt2bSEkJETR8uoivRNvZWUlbNq0qUSav//+W9xvzM3NdbYY0NUCd+nSpeL3r7zyinDr1i21+RQUFAgXLlwQpk2bpvbusJLWXeoYer+SbjtLS0vB1tZW2Lhxo9p9S9oKavLkyRrzvHLliqx1YNu2bYWgoCC1aWNjY4Xvv/9ebWvh/Px8oWfPnmI+7dq1U3vHOCsrS5g3b56gUqkEAIKtra3WZVZKSWuQItL9uWi/Gj58uBAfH19imYqenIiMjJSdAzp06CBERESUSP/DDz8IJiYmYrp3331XYzmkx2sLCwuhbt26wpkzZ2Rp8vLyxFZHQGEL0KJzlo+Pj3Dv3j1Z+uTkZNl28PHx0bXqFJk2bZrsmL5kyZISLVsjIiJkLezt7e01blt9W1gKgny7FZ0zJk6cWOI8kJGRIYwePVpMa2dnp7GVRnnUW+l+amZmJlhYWAgrV64ssZ8WPw8rVR51ccWKFbL6OGLECOHBgwdq016/fl2YOXOmcOTIkRLf3b9/X/bbp2HDhsLhw4fV5pOUlCT8+uuvalvTlXcL3KLzS7Vq1dS2JD548KCsJbKPj4/Gc+7q1auF5cuXa33iJyQkRNYi+auvvtKYtjT7hpJpnj17JrRu3VpMV6NGDbXb8MiRI7Jt2K5dO41PkEnPwZaWloKpqanwww8/CLm5ubJ09+/fF1q0aCGm7dmzp9r8DHXOLr5OeBlYElvg/p/nuUWfoVrg/vbbb7L95fPPPy+x39+/f1/o0qVLies2JS1wi9J//PHHJZ4ISUxMFHx9fcW0bm5uGo+3LzNN9dQYWsVW5Dye5/21SN++fcU8bGxshM2bN5dIc/HiRXEdAhDq168vPH36tES6LVu2iGlGjx6tdn5jxowRgMLr76Lfj5rSSuMS6q7VKxLP3C+xjRs3ihWxZs2aJX5Y3rlzR3bSCgsLU5vPuHHjxDSffvqp1nnu27dPTOvu7q42zZdffimm8ff3L1Gu4iZMmCCm//bbb9WmkS6HSqUS/v33X6156mvr1q1i/h9//LHaNImJibIT+w8//KAxv4cPHwpVq1aVlVtTAFd6cl+wYIHWcqanp8uC45oCUkqVJoCrD+mB/ObNmyW+l9ansWPHlmlemn7IJSYmCt7e3rJ6e+fOnTLNq8itW7dk21jbCeHMmTOyoIO2H5y6ArjSR6L//vvvUpe/tAFcpZTsV4Ig33YqlUrrI8z79+8X09rZ2Wk8vhT9GAcKA0FpaWmlWoYNGzaI+bRp00ZnPnPmzBHTT506tVTzlCptABeA0Lt3b52P2UuPv25ubmp/RBVZsmSJmNbExERjoE9aBmtra7X7viAUBuOKP3bYvHlzISsrS236+/fvi4FplUolxMTEaF02XSIjI2X75LJlyzSmTUpKElxdXcW0mm4elDWAq+3HpyAUBlzr168vpt26davadOVRb6X7aXn8ADZ0XUxKSpLd5C7L/jh27FjZsTIuLq5U+ZR3ALdofRS/YSJ18uRJMWAPlLz5r6/k5GSxywtnZ2chLy9PbbryCuCuWbNGTGNubi5cunRJY34XLlwQjyEAhPXr16tNV7ybqd9++01jntevXxfXp6bjkqHO2YLAAK4uDOD+H2MKCHl6egrTp09X/Cftqqu0Adzc3FzB2dlZTKPtZl9qamqJbuyUBHAB7dfOcXFxYmMGQ1y3vYjU1VNjCqxW1DyMaX8tTUzg2LFjsv3izz//1Jg2KipK1lXe/PnzS6SJjY0Vv3d2dlabT1GjHl9fX7Ghlqa00t/OZe2Ksqx45n6J9erVS6yI7733nto00iCGphPM4cOHZRcI2u4OSvsJVNefT05OjlCzZk3xIiI6Olrncjx69Ej88duyZUu1aaQHhBEjRujMU195eXninZt27dqpTbN8+XLZxaWmi5Qi0oM2oD4Qd/XqVfF7Dw8PnXkKgvyOlLYfI0qUdwB327ZtWoMjmzdvFr//z3/+U6Z5qfsh9/DhQ+GVV14RP2/durUQGxtbpvlIffTRR2LenTp10pleevdP0w9OQdAdwH311VfF769evVrq8pd3AFfJfiUI8m03YMAArXkWFBTI+sdU1xI8KChI/F6lUuns61GbNm3aiHmdOnVKZ/qsrCzx5o2Dg4Oifkq1KUsAV1PgtMjTp09lN6V27typNX1+fr6sT91PPvlEbTppGd5//32teUoDhwCEvXv3ak3fvXt3Me2+ffu0ptVl9uzZYl6tWrXS2TJGejyztLRU+8RIWQO4FhYWOo9RH3/8sZj+ww8/VJumPOqtdD/18vJStGxKlUdd/Pbbb2XHt9K2DH748KEs6KftBpMuFRHAnTBhgs58pQHpwYMH67kUJUlbsl+7dk1tmvIK4Hbs2FGv30TSsmo6b0vPwZp+l0p5eXlpPS4Z6pwtCAzg6sIA7v8xpoBQWf5KG8CVNhKxs7MTUlJStJZXek2i7TeX9HdZjRo1NN50LiLtf3f58uVa076MitdTYwusVtQ8pI2CKnt/LU1MQBoj0nUtJwiCsGjRIjG9s7Oz2t/gTZs2FdMUf3IlPDxc/O7LL7+U5Vc8bWRkpPhd48aN9V42Q2MfuC+phw8f4tixY+L7cePGqU03fvx4cfTLjRs3YsGCBTAxMZGl6dWrF2rXro24uDjcu3cPZ86cUdu3YFpamqwPIXXzvHTpkthHn7e3N1xcXHQuS506ddC0aVOEhYUhNDQUycnJqFq1qsb0o0aN0pmnOtevX8eVK1cQHR2N1NRUPHv2TPZ90Yjp169fR0FBQYn1JO07ZcSIETA1NdU6v7Fjx4r93Why8OBB8XVAQIDOPAHA19dXfH369Gmd6ctTZmYmgoKCcP36dcTHxyMtLU3sMw+AbIRHdaPeN2jQQHy9c+dOzJ49G7Vr1zZI2cLDw9G7d2+xv9Zu3bph3759cHBwMEj+QGE/O0XGjx+vM/2ECROwcePGMs9Xut5++eUX/Pbbb2XOs7TKul8VFxAQoPV7lUqF1q1bIy4uDkDhCOwtWrSQpSnq1xso7Df2lVdeUbw8UrGxsWK9rVu3rqI+V62srODt7Y1Dhw4hJSUFoaGhaNWqVanmXxatWrVCs2bNtKY5e/asuL2qVauGgQMHak1vYmKCKVOm4MMPPwQgr/+aDBs2TOv30m1nbW2Nvn376kx/8uRJAEBUVJTO+WsjPYdOnjxZrKuaDBkyBE5OTkhKSsKzZ89w7tw5+Pv7l6kMxXXt2lXnMbBt27bia3Ujb1dEvS3teViT8qiL0uPAm2++WaJvVqX++ecf5OXlASjso9XQ29zQlPRlPnHiRHE8gxMnTkAQBK31/8mTJwgKCkJYWBiePn2KjIwMWd+rly5dEl9fvXoVLVu2LMMSKJeeni6b95QpU3RO88Ybb2DFihUAgIsXLyIjIwO2trYa0+s6JwGF+2RRP9rq9klDnrMnTZqESZMmlXp6opeF9Lqtf//+sLe315p+yJAhsLa2RlZWluJ5DBgwQOd4AG3btkVgYCAA9ccH+j/ScW6MpT/aipqHsf+20EX6O0zJuXjy5Mn49NNPUVBQgNjYWISHh5cY58PHx0ccB+T48eNo0qSJ+J10/+7ZsycsLCxkZdGUttL7vwXAAO5LauPGjSgoKABQOBhEhw4d1KYbMWIE3nvvPTx79kwM+vbq1UuWxtTUFKNGjcKPP/4IANi0aZPaC75du3aJJzUvLy+1g02cO3dOfJ2QkIAZM2YoWp7k5GQAgCAIePTokdYAbvv27RXlWWT9+vX4+uuvERERoSh9bm4uUlJS4OjoKPtcGoBUN9hXcS4uLqhRo4ZsUI3ipOvr3LlzitaX9KKpaECMipaUlIQ5c+Zgw4YNsgFdtFE3MEfHjh3h4uKCe/fu4cGDB2jevDkmTpyIAQMGoFOnTrC2ti5V+S5duoRp06aJ8xwwYAACAwMVDbqklCAIuHbtmvi+Y8eOOqfx8vKCSqUq86AjI0eOxOrVqwEAK1euxMWLFzFx4kT06dOnxMmvvBhqvypOyYV/tWrVxNdFA0RISQfpUjIwoibS/VMQBMXHM2nn/Q8ePKiUAK6S42RwcLD4umPHjjAz0/2TQjrQQHBwsM7AT/PmzbXmJ60PHh4eOssgTZ+amqqruBoJgiA7pqsbQKE4c3NzeHl5iYHBK1euGPwHtyHqf0XUW33Pw7qUR10sGigDKNtxQHo8MYYf/tqoVCpF56KOHTuK56Lk5GRER0erHRDy5s2bmD17Ng4dOiS7OauNrkG4DCkkJEQsl52dnaJjbZs2bWBra4uMjAzk5+cjJCQEnTt31pjeEPukMZyziSrT3LlzMW/ePMXp582bJwbySkvf6zZra2u0aNFCNlC1LoY4PtD/eVmDtz4+Ps91cP/Ro0diAz4AWs+pRWrUqAEPDw8xQHvlyhW1AdyiQVmPHz+OqVOnit8VBWVtbGzg5eUFU1NT2NvbIzU1tURaaXDZGH7HMYD7kioaOR7Q3vLP0dER/fv3x86dOwEUBl2KB3CBwta0RQHc7du3Y/ny5TA3N5elKWqtUZRenaLRoYHCFpBFowfq4+nTp1q/r1GjhqJ8BEHA66+/jrVr1+pdhrS0tBKBJulFSb169RTlU7duXa0BXOn6OnbsmKxFmBK61lV5uHfvHrp37y62bFVKXaDX3NwcmzZtQr9+/ZCamoqkpCQsXboUS5cuhYWFBdq1a4fu3bujX79+6Natm84WckVGjx4ttpgaO3Ys1q1bpyggoI+UlBTk5OSI76UtbDSxt7eHg4ODeMOitF599VW8//77WLp0KYDC4EVRAKR69ero0qULfHx8MGzYMJ0/NPRl6P2qOCUtpKXHptzc3BLfP378WHytaYR1JaT7Z0xMDP73v//pnUdl7KOAsuOk9Nik5GkJAHB1dRVf5+TkIC0tTWurFl3bU7pfKtn20vTqtr1SKSkpsulLs/zlEagyRP2viHqr9DyslKHrYmpqqqwVVVmOA4Y6nlQER0dH2NnZ6UxX/FwUHx9fIoB75MgRDBo0qMRTFboovalrCNJ6U79+fZ1PeACFrbfr168vXjTq2o8NsU9W5jmb6GVV2us2fQK4hjg+kJyxBVYrah6urq7PbRBXei62trZGzZo1FU3n6uqq9VwsDbZKW9ECwL///gugMFhc1Pq2a9euOHjwoMa0xfOsLLp/qdAL5/z582JlV6lUGDt2rNb00gDvrl271P64bt++vfi4bVJSEg4dOiT7Pi4uTgwumpmZYeTIkWrnZYg7i0WBN02UtspctWqVLMjUv39/bNy4EaGhoXj69CmePXsGobAfaQiCILtoLGrdLJWeni6+trGxUVQGbY/lAWVfX0pbxBjS2LFjxeCtvb09PvzwQxw5cgRRUVFIT09Hfn6+uE6ld7zUrVOg8GAbEhKCKVOmyNZXTk4OgoKC8N1336FHjx5o2rQpdu/eraiM0h9LUVFRej0OpZS0PgCGqxNKLVmyBHv37i1xlzMhIQF//vkn3n//fbi4uGDYsGG4d++eQeYJGH6/Kk5pkF4b6TFOSTBDk4o4npUXJcdJaR1WWi+Lp9MVrNFnexpi2ytVfP8tzfKXR6DKEOvAmM7DShm6LhbfNmU5DhjqeFIRlJ6HAO11OT4+HiNHjhSDtw0bNsSiRYtw5swZxMTEIDMzEwUFBeJxfu7cueK0So7zhlKaelM8rSGPYdpU1jmb6GVVHtdtxVXk75aXwdy5c40usFpR85B2+/S8Ka9zca1atcT41JMnT3Dz5k0AQEREhNhYQfqEVVFwVpr29u3bePjwIYDCJ/3q1KmjuHzlhQHcl9D69evF14IgwNXVFSqVSuPf4MGDxfSZmZnYsWOH2nylgWBpa1sA2LJlixgs7N27t8Y7K9Id8T//+Y8skKP0z1B3RhYvXiy+XrhwIfbt24dx48ahefPmqFq1qqyvFED3j3jpsmVmZioqQ0ZGhuI89+zZU6r1VZHOnj0r9qlcpUoVnD9/HosXL0bv3r3h6uoKW1tbWQsYpQEOV1dXrF69GvHx8fjnn38wZ84c9OzZUxYkiIiIwNChQ7FkyRKd+W3btg3Vq1cXy+zv72/wYEvxC3lD1Ql9DBgwAGfOnMHDhw+xadMmvP3227L+XgVBwK5du9C+fXvFXR3oYuj9qjxUqVJFfF08UKcP6f45ePDgUu2fxtxXobQOK62XxdNJ1/XzpPj+W5rlN9Zlfx7rraHrYvFtU5bjgKGOJ6WhbzBU6XkI0F6XV61aJd4IaNu2La5du4aPP/4YnTt3hrOzM6ytrWWBi8o4zgOlqzfF01bkflwZ52yil1V5XLdR+froo4+0fv+iBm9PnDihuJW4MSrPc7E0LlTUMEzaQEwawJW+LkpjbP3fAgzgvnRycnKwbdu2MuUh7X5Baty4ceIP8n379sn6F9y0aZMsnSa1atUSX9++fbtM5SyLBw8eiPN3dHTExx9/rDV9amqqzkdGiwKCAMQ7ObpIB/FSx1jWl1JHjx4VX0+aNEln3236tiKxtraGn58f5s+fj2PHjiExMRE7d+5E69atxTSffvqpzvXasmVLHDt2TBbE7du3r0Evvh0cHGTBSiVdSqSmppZL/1d169bF2LFj8euvv+LGjRt48OABvvrqK/HHa2JiIj744IMyz6c89qvyIN2vyjLQ1fO2f+pL+hi80i5RpPu0hYWF0QYxdXFwcJC11C/N8kvPCcbkeay3hq6L9vb2shuAhjoOlHXgPGmdU9I6X9/zxdOnTxUFU4ufi4rXZem5/osvvtDZ8riyWoxK682DBw8U3dQuKCiQjR9QGftxRZ2ziV5m5XHdRpXnRQ7ePu9d50jPxVlZWYq7GFPym1pdALcoKGtnZwdPT0/x+7Zt24rdmqgL9jKAS5Vi3759SEpKAlB4IdCxY0dFf9IBR/7991+1fay4uLiIg4JkZWVh165dAIBbt27hypUrAAp3lEGDBmksn3TwjH///VfvvtMMRdoHYJMmTXT2f3r69GmdP/zbtGkjvi4abVibe/fuae3/FpCvryNHjujM09D0ffRHul51DU4EQBwtvrSsra0xdOhQHD9+XByZPScnR9G6atmyJY4ePSqeEM6cOWPQIK5KpZINmCId6EaTCxcuVEir6Xr16uGLL77AqlWrxM/++uuvEvtjWba/ofar8tCpUyfxtb79SmvK58aNG4ovAJ4Xbdu2FV9fuHBBUZcsRS3wi6Z/Xh8fVKlUsmP62bNndU6Tl5cnO/a3a9dObb6V7Xmst+VRF6XnV0MdB6QXAqUhDTInJibqTH/9+nW98hcEQTZ4mybnz58Xj81Vq1Yt0f+tPuf6/Px82bbQpDz2jdatW8PU1BRAYetoJesrJCREbPVjamoqu0FcWZSes4lIOX2v27KyshAaGlqOJaLSMtbAKoO3herWrSt7OlvJb+rExETZWEnqflMD8qDrv//+C0EQxD5tu3TpIrsWNTU1Rbdu3dSmLZ5XZeIgZi8ZafcJ/fv3F4OsSnh4eOD27dsQBAEbN27Ef//73xJpxo0bh9OnTwMo7EZh0qRJsta3Q4cO1dqPUJcuXVC1alUkJycjPT0dq1atUjwCtiFJH+NX8tjMihUrdKbx8fER13dgYCAWLFggXjioU7wbCnX69++Pr776CkBhi5fr168rGtHUUKysrMTXSjrW12e9xsTEYO/evaUvnISjoyM6d+4srn/poDLatGrVCkePHoWfnx8SEhJw+vRp9O3bF4cOHTJIX4Y9e/bEpUuXABS2Up8+fbrW9NL9tyL0799ffJ2bm4ukpCQ4OzuLn5Xn9geU7VfloW/fvuJox0ePHkVYWJjYh5I+GjZsiGbNmiEsLAwA8OOPP8q6kHjede7cGZaWlnj27Bni4+Nx4MABDBw4UGN6QRBkddjX17ciillufH19xcFK1q9fj3fffVdrkGnv3r1i0M3Kygre3t4l0ui7T5WH57Helkdd7Nu3r9hKY9WqVZg1axYsLS31Lturr74KMzMz5OXl4fbt2zhy5Aj69Omjdz4AZIHSkJAQCIKgtc4FBgbqPY+NGzeqHaxWat26deJrHx+fEmXQ51i/Z88exMXF6SxXeewbdnZ26NChgxi0Xrdunc5ulqR9uHt5eRmsX3pD0HXOJv189tlnJbp10lfRsbM8j6HlNQ/pQLsvIx8fH/F4sH//fqSmpmoddHX37t3lMmYGlY2xBlYZvJXr2bOn+JT4unXrtP6GAwp/dxd1E1WnTh00adJEbbqaNWuKv2kTEhKwc+dOxMbGivMszsfHB/v37xfTFt2Q9vDwMJ7zqUAvjSdPnghmZmYCAAGAsGPHDr2mnzdvnjhto0aN1KZJSkoSLCwsBACCiYmJ8OjRI6Fhw4bidH/99Zde86lSpYpw7do1xWWMi4tT+3lRfkqrfHx8vKBSqcTliIyM1Jh269atsvwBCFFRUSXSJSQkCJaWlmKaJUuWaMzz4cOHQtWqVWV5Hj9+XG1aHx8fMU2rVq2ElJQURcv47NkzISkpSVFaTVatWiXOe+LEiTrTL168WEzfo0cPjeny8vKEfv36yZZfXfqEhATFZe3QoYOY16pVq0p87+LionH7hYSECNWqVRO/79atm5CWlqZ43pqEhYXJlnHTpk0a0545c0YwMTHRWc8EQRB69Oihtd7Ex8crKl9oaKiYj4mJiZCdnS37Pjs7WyyTmZmZkJOTozW/8tivBEH7tlNn4sSJYvq1a9eqTdOlSxcxTYcOHUq9vdetWyfmY2ZmJvz999+Kp42NjS3VPKWULGuRuXPnimnnzp2rKP8JEyaI03h4eAipqaka0y5fvlxWn+7evas2nT7H6+PHjys6phQpzTJqEhkZKdsnf/31V41pk5OTBXd3dzHt5MmT1ab7+++/9VoeQdB/mZSss/Kot/rup/oydF1MSkoS7OzsxHRTp04tddnGjh0r5uPi4qLxt4ouubm5QpUqVcS8Dh8+rDHt/v37ZfuSi4uL2nRRUVGydCYmJsKZM2c05nvy5EnxOA5A2L9/f4k0/fv3V1Qnnzx5ItSvX182f03pS7NvrF27VudvlDVr1ohpLCwshJCQEI35XblyRTA3NxfTr1+/Xm06Xefg4nTtw4Y6Z5Nu6enpJX578A9Cenp6hW8L6X6k7/lauk9pOl7oOifl5uYKtWvXFtPMnDlT4/xSU1Nl53hA828ufX6XCYKy49jLTLrPFq+n9+/fF9zc3AQAgpubm3D//n2d+S1cuFDMb+HChTrTG8s8tK2HilCW/VUQBOHYsWOy/efQoUMa096/f19wdHQU086fP19r3u+8846YtkWLFuLr8+fPl0h7+fJltWnffvttvZepvLAF7ktk8+bNYp9p9vb26Nevn17Tjx07VmyVFhkZiTNnzohdJhRxdHTEa6+9hj179qCgoADvvvuu2Oebs7OzohZXH374IbZv344bN24gLS0NXbt2xeLFizFx4kS1d8ITExOxe/durFixAj179jTIHejq1aujY8eOCAoKQkFBAQICArBlyxbZ3Z2CggKsWLEC77//PkxNTWFubo7s7GyNeVarVg3vvfcevvvuOwCFHa1bWFjgnXfekbVeuXHjBgICApCcnCy2KNJm+fLl8Pb2Rnp6Oq5duwYvLy/8/PPPGlvQREZGYtu2bfjll1/w22+/yVps6Eva2vfIkSNISUkR+45Rp1+/fvjoo4/ERxJmzZqFr776StbXYFxcHKZOnYoDBw7A1tZWa2fmy5cvx549e/DWW29hyJAhau+MpaWlYcGCBWJLV1NTU71bP7Vq1QrHjh2Dr68vEhMTcerUKbz22ms4dOhQmVrfNG3aFOPHj8fGjRsBAG+88QYA+YCAQGEr0FGjRqGgoADm5uZlbn3k7e2NDh06YPz48ejVq5fa/SosLAwTJkwQ3/v5+ZVofWZpaQkPDw/cunULeXl52L17N0aMGKFxvuWxX5WXZcuWoXPnznj27BkuXbqE7t2749dff4WXl1eJtHFxcdi0aRMEQSgxgMK4ceOwYcMGHDt2DHl5eejXrx++/PJLzJgxQ23dSU9Px759+7Bq1SpUq1YN27dvL7dlNIS5c+di165dSE9PR0REBPr06YNNmzbBzc1NTFNQUID//e9/eP/998XPpk+fXuKx6+eNu7s73n77bbGl+IwZM5CXl4dp06bJWiDeuXMHY8aMwZ07dwAUnn/VPcECyI+p58+fx/3799GgQYNyXAr1nsd6a+i66OjoiEWLFolPRvz6669ISkrCDz/8oHawkBs3bmDlypXo168fevfuLfvum2++weHDh5GYmIh79+7B29sbK1asUHsuSk5ORmBgICIjI8XfC0XMzMwQEBCANWvWAADefPNNHD58uMRAVps2bcLUqVMV/YaQKjq/DBw4EJs3by5RvsOHD2PMmDFi9wndu3fHa6+9ViKf/v37Y//+/QCAb7/9Fo0aNSox/sGVK1cwbtw4PHjwQOe5Hii/fWPs2LH46aefEBISgpycHPTp0wd//PFHiZY5R48exZgxY8Tzb7t27TB69Ogyz18JQ52zgcKWTZMnTxbfF21LKmnmzJkGa4ELALNmzSprkSp0Hjk5OVi2bJnB8nvemJmZYc6cOXjnnXcAFP4urFKlCubOnSvrj/zBgwfiOV7fYy6VH2NtFcuWt+r17NlTfMoVgPhbJyAgQJYuODgYI0eOFMdIqV+/PmbOnKk1bx8fH/zyyy8AIHZzUqVKFVkXoUXatGkjPg0u7RLFWLpPANiFwktF+rjgsGHDZI+jKdGoUSN4eXmJ/QCtX7++RAAXKLzw27NnDwDIumgYPXq01i4DitjZ2WHv3r3o1asXoqKikJqairfeegsfffQRvL29UbduXahUKiQlJSEsLAzh4eFiE3p1TeFLa8GCBejduzcKCgoQHByMli1bokuXLnBzc0N6ejpOnTolNsFfuHAhVq5cqXMgjnnz5uHo0aO4fPky8vPzMWPGDHz33Xfo0qULbGxsEBERgTNnzqCgoABDhw5FYmKi2PeKNCAg1aJFC2zZsgUjR45EZmYmwsPD8eqrr6J+/frw9PRE9erVkZOTg/j4eISEhBi0P0NPT080aNAA9+/fR1xcHJo2bYrevXujevXqYlDa09MTI0eOBPB/AcuigfB++OEH/PHHH/D09ETNmjURHR2NkydPIicnB1WqVMH333+PqVOnai1DSEgIpk+fjhkzZsDd3R0tWrRA9erVkZubi5iYGJw9e1Z2YfjJJ5+U6sSnKYh78ODBMgVxly5dinPnziEyMhLZ2dkYN24c5s+fj06dOsHU1BQhISEIDg4GAPznP//B7t27yzzgS25uLrZu3YqtW7fC2toarVq1gpubG+zt7fH06VPcuXMHly9fFtNbW1trvDEybNgwLFy4EEDhvr9+/Xo0atRI9uNWOm157FfloV27dli9ejUmTZqEvLw8BAcHo2PHjmjSpInYyX1KSgpu3ryJ0NBQFBQU4L333iuRj6mpKQIDA/Hqq68iODgYOTk5+OSTT/Dll1+iU6dOaNCgASwsLPD06VNERETg5s2bYoBg2LBhFb3YenNzc8Pvv/+OsWPHIj8/H+fOnUOTJk3QrVs3uLu7IyMjA6dOnZIddzp16lQiMPW8Wrx4MS5duoSLFy8iLy8PM2bMwLfffouuXbvCzs4Od+7cwcmTJ8U+Wc3MzLB69WqNwetatWqhS5cuOHPmDLKzs9G6dWv4+/vD2dlZPAe4u7tj2rRp5bpcz2O9LY+6+M477yA0NFQM0gcGBmLnzp3w9PSEh4cHrKysEB8fj+DgYHFsAHW/Q+rXr49t27Zh8ODBSE9PR1RUFPz9/eHi4gIvLy84OTmJgeerV68iNzdX43gB//3vf7Ft2zZkZGTgwYMHaNOmDXr06AE3Nzekpqbi7NmzuH//PkxNTfHbb7+JNwaVqFOnDoYOHYqlS5fC398frVq1EvsXvnLliqyP2Jo1a2LNmjVqu3CYNGkSlixZgoiICDx79gzjx4/H119/jdatW8PKygqhoaHiTdXWrVujT58+Oo8J5bVvWFhYYMuWLejRowfi4+MRFxcHX19ftG7dWuwD8+rVqwgJCZEt+5YtW2TnufJkyHM2Kff555+XuYuMoiDozJkzxUYwhlZe88jIyHipA7gAxAYlBw4cAPB/v0t9fHzg4OCA6Oho/Pvvv8jNzUXHjh3h7u6OP/74A4Dm6zYqP0XXew8fPoS/vz+io6Ph6uqKgwcPwsnJSeuNwu+//x7z588HUHhD+L333tOa3tjmoesmaEX69ddfxViQEl9++SUGDhyItWvXokuXLrhz5w7S09MxYsQING7cGB07doSFhQXCwsIQFBQk3ni0tbXFli1bULVqVa35qwu+duvWTW1cysTEBN27dy/RjaMxBXDZhcJLIiQkRNYs/Z9//ilVPsuWLRPzcHBwELKyskqkyc7OLvH4PwDhypUres0rMTFRCAgIkD2qp+2vatWqwrp169TmJU2njxUrVsi6nSj+Z2JiIsyZM0coKChQ/HhoYmKi0L17d63LMmDAACElJUXo3Lmz4vV39epVoX379oofh3J1dRWCg4P1Wh/qHDhwQNY1RPG/4o/7ZGRkCL1799Zatnr16gmnT5/W+aivtEsGXX8WFhZaH7FQuv2uXr0q606he/fuZX5U5f79+0Lbtm21ln/y5MnCs2fPFJVT1+Ob0kdCdP01bNhQ6+O0KSkpwiuvvKI1j+IMvV+VRxcKRY4ePSrrBkbb3+eff64xn8zMTGHq1Klal1v6Z21tLXz99dc6l8WQy1qW7gX27dsn1KpVS+dyjR49WsjIyNCal7a6U1xldqFQJC0tTRgxYoTOZXd2dhYOHjyoM7/Lly8L9vb2GvMpvpzl0YVCEUPW2/LuQqGIIetikR9//FHrNin6U6lUwpEjRzTmc/XqVaF169aK1uXYsWM15nPo0CHBxsZG47T29vbCzp07Zd0jKOlCwcXFRcjJyRHefPNNrWVr1KiRcP36da3rLDw8XHzkU9Nfly5dhIcPHyquw/ruG/o8ehweHq7zPAxAaNeundbufwTB8F0oGPKcLV0nSo6xLxt2oaD+73l7JNsQXSgUycrKEoYNG6Z1/Xh7ewuPHj0SxowZI362a9cutfmxCwXD4j5rfPurvn/S/SAuLk7w9fXVOU2jRo2ECxcuKC5f8WvV77//XmPaJUuWyNI2adKkLKvG4NgC9yUhbX3r7Oxc6paqI0eOxAcffIC8vDykpKRgz549GDVqlCyNpaUlAgICZKPhNmvWTDZKtBJOTk4IDAxEaGgotmzZghMnTiAqKgqJiYkwMTFB1apV0ahRI7Rr1w69evXCq6++qnerYl2mTp2KLl26YOnSpTh+/DhiYmJgbW2NunXrwtfXF1OmTCnVcp04cQJ//PEHNm7ciODgYCQnJ6NWrVpo1aoVJk2ahGHDhomtjIvourvUunVrXLp0CX/99Rf27NmDM2fOICYmRuyKoUaNGvDw8ECnTp3Qp08feHt7G2RU59deew2XL1/Gzz//jNOnT+PevXtIT0/X+FiejY0NDh06hD/++APr169HcHAwUlNTUb16dbi5uWHYsGGYNGkSHB0dxQFkNPnwww8xbNgw/P333zh79iyuX7+O6OhopKaminWkWbNm8PX1xYQJE+Di4lLm5W3durU4sFliYiJOnjyJfv36iV0+lEb9+vVx4cIFrF27Fps3b0ZoaCjS09Ph7OyM9u3b4/XXX0ffvn3LXPYiV69eRVBQEI4fP44LFy4gPDwcMTExyMzMhI2NDWrXro02bdpg4MCBGDFihNaBe+zt7XHhwgWsWLEC+/btQ1hYGJKTk7V281Ae+1V58fX1RXh4OLZu3Yr9+/fj0qVLePLkCZ49ewYHBwc0atQI3t7eGDJkiDhyqTrW1tZYsWIFZs+ejU2bNuHYsWOIiIhAYmIiCgoK4ODgADc3N7Ru3Rp+fn7w9/fXOliGsenfvz8iIyOxZs0a7N+/Hzdu3EBCQgKsra1Rp04d9OzZExMmTEDHjh0ru6gGZ2dnh23btuE///kPNm7ciBMnTiAmJgZZWVmoXr06WrRogf79+2PKlCmKjhHt2rXDtWvXsHz5chw7dgx3795Fenq62Iq3Ij2P9bY86uJ7772HcePGYd26dThy5Ahu3ryJhIQEAIVdwzRr1gw9evTAyJEj0bhxY435tG7dGsHBwdizZw/27NmDc+fO4fHjx8jIyIC9vT3c3Nzg5eWFAQMGaO3qx9/fH7du3cLixYtx5MgRPHjwAKampmjQoAEGDBiAadOmoUGDBmKrYH2Ym5tj5cqVCAgIwOrVq3HhwgXExsbCxsYGTZs2RUBAAKZOnarz95aHhweCg4Pxv//9D7t27UJ4eDhycnJQu3ZttGzZEmPGjEFAQIBsBGhdynPf8PDwwKVLl7Bjxw7s3LkTFy5cwJMnTwAUtrjt2LEjhg8fLv4+q0iGPGcTkX6srKywY8cOHDhwAKtXr8b58+eRkJCAatWqoVmzZhg7dizGjx8Pc3Nzva7biKikWrVq4ejRozhy5Ai2bt2K06dPIy4uDrm5uahZsybatm2LwYMHY9y4cXo9BdOzZ0/cvHlTfK+tRW3xOJlRtb4FoBI0RVmIqFJlZWXB3t4eeXl5sLGxQWpqqqIuKIiIiIiUiI6OFrv0cHFxKVXQl+hFIQgCMjMzK7sYRsfGxqbCb1w8j+rWrSuOWh8bG4vatWtXcolefNxnS+L++mJjC1wiI7Vr1y5x0Ll27doxeEtEREREVE5UKlWZ+72ll1PRk48AUK9ePQZvKwj3WXrZsHdtIiOUnJwsG6W8okY6JiIiIiIiImVyc3PxwQcfiO953UZE5YUBXKIK9s4772DdunVIT09X+/358+fRrVs3REVFASjss3jcuHEVWUQiIiIiIqKX2pw5c7Bs2TIkJiaq/T4sLAy9evXChQsXABQ+vv7OO+9UZBGJ6CXCLhSIKtjNmzexYsUKTJs2DW3btkWjRo1gY2ODlJQUBAcHIzw8XExrZmaGNWvWVPqgMERERERERC+T+/fv46uvvsKHH36I1q1bo0mTJrC3t0daWhpCQ0Nx7do12cDNP/74I1xdXSuvwET0QmMAl6iSZGdn49y5czh37pza72vWrIn169fD39+/gktGREREREREAJCXl4fLly/j8uXLar+3t7fHsmXLMHHixAouGRG9TFSC9JYREZW7uLg47N69GydOnEBERATi4+ORmJgIExMTVKtWDa1bt4a/vz8mT54MGxubyi4uERERvaCio6PRsGFDAICLiwuio6Mrt0BEREYkOTkZe/bswbFjx3Dz5k3Ex8cjPj4egiDAyckJzZs3R69evfDGG2/AycmpsotLRC84BnCJiIiIiIiIiIiIjBQHMSMiIiIiIiIiIiIyUgzgEhERERERERERERkpBnCJiIiIiIiIiIiIjBQDuERERERERERERERGigFcIiIiIiIiIiIiIiPFAC4RERERERERERGRkWIAl4iIiIiIiIiIiMhIMYBLREREREREREREZKQYwCUiIiIiIiIiIiIyUgzgEhERERERERERERkpBnCJiIiIiIiIiIiIjBQDuERERERERERERERGigFcIiIiIiIiIiIiIiPFAC4RERERERERERGRkWIAl4iIiIiIiIiIiMhIMYBLREREREREREREZKQYwCUiIiIiIiIiIiIyUgzgEhERERERERERERkpBnCJiIiIiIiIiIiIjBQDuERERERERERERERGigFcIiIiIiIiIiIiIiPFAC4RERERERERERGRkWIAl4iIiIiIiIiIiMhIMYBLREREREREREREZKQYwCUiIiIiIiIiIiIyUgzgEhERERERERERERmp/weIAS8h90CeewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1350x1020 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patterns = [\"////\", \"+\"]\n",
    "\n",
    "plt.figure(figsize=(9,6.8), dpi=150)\n",
    "\n",
    "\n",
    "ind1 = np.arange(len(df_late1wide))    # Array for horizontal bar's position\n",
    "width1 = 0.4\n",
    "# fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "\n",
    "ax1.barh(ind1, df_late1wide[1], width1, color='white', edgecolor='black', label='High', hatch=patterns[1])\n",
    "ax1.barh(ind1 + width1, df_late1wide[0], width1, color='white', edgecolor='black', label='Low', hatch=patterns[0])\n",
    "ax1.set(yticks=ind1 + width1/2, yticklabels=np.array(['Low', 'High']),  \n",
    "ylim=[2*width1 - 1.3, len(df_late1wide)],\n",
    "xlim=[-0.2,0.6])   # Set Y-axis ticks and ticklabels\n",
    "plt.yticks(rotation='vertical', fontsize = 15)\n",
    "plt.axhline(y=0.72, color='black', linestyle='--')  # line \n",
    "plt.title('Panel A: Exposure to ICT, 1980-2019', fontsize = 18, x=0.5, y=1.02)\n",
    "# ax.legend(bbox_to_anchor =(0.5,-0.3),ncol=2, loc='lower center')  # Legend \n",
    "# plt.ylabel('Exposure', fontsize = 11, loc='top', rotation=0)\n",
    "plt.ylabel('Exposure', fontsize = 15)\n",
    "plt.axvline(x=0, color='gray', linestyle='-' )\n",
    "plt.xticks(fontsize=14)\n",
    "h, l = ax1.get_legend_handles_labels()\n",
    "ph = [plt.plot([],marker=\"\", ls=\"\")[0]] # Canvas\n",
    "\n",
    "#================= Panel B - early period\n",
    "\n",
    "ind = np.arange(len(df_early1wide))    # Array for horizontal bar's position\n",
    "width = 0.4\n",
    "\n",
    "ax = plt.subplot(212, sharex=ax1)\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "ax.barh(ind, df_early1wide[1], width, color='gray', label='High', edgecolor='black', hatch=patterns[1] )\n",
    "ax.barh(ind + width, df_early1wide[0], width, color='grey', label='Low', edgecolor='black', hatch=patterns[0])\n",
    "ax.set(yticks=ind + width/2, yticklabels=np.array(['Low', 'High']),  \n",
    "ylim=[2*width - 1.3, len(df_early1wide)],\n",
    "xlim=[-0.2,0.6])   # Set Y-axis ticks and ticklabels\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(rotation='vertical', fontsize = 15)\n",
    "\n",
    "plt.axhline(y=0.72, color='black', linestyle='--')  # line \n",
    "plt.title('Panel B: Exposure to Manufacturing, 1900-1940', fontsize = 18, x=0.5, y=1.02)\n",
    "\n",
    "# plt.plot([], [], ' ', label=\"Average task distance from other occupations:\")\n",
    "\n",
    "# ax.text(0.027, -0.5, r'Average task distance from other occupations:', fontsize=12.5)\n",
    "# ax.legend(bbox_to_anchor =(0.5,-0.75),ncol=2, loc='lower center', title=\"Average task distance from other occupations:\", fontsize = 12, title_fontsize=12)  # Legend \n",
    "\n",
    "handles = ph + h\n",
    "labels = [\"Average task distance from other occupations:\"] + l  # Merging labels\n",
    "plt.legend(handles, labels, ncol=3, bbox_to_anchor =(0.5,-0.5), loc='center', fontsize = 16, title_fontsize=11.5, frameon=False)\n",
    "\n",
    "plt.ylabel('Exposure', fontsize = 15)\n",
    "plt.xlabel('Log change in employment share', fontsize = 18)\n",
    "plt.axvline(x=0, color='gray', linestyle='-' )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(output_dir + '/Fig2_barh_dist_exp_emp.jpg')\n",
    "plt.savefig(output_dir + '/Fig2_barh_dist_exp_emp.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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